Category Archives: python

Ridge Regression in Python

Ridge regression is one of several regularized linear models. Regularization is the process of penalizing coefficients of variables either by removing them and or reduce their impact. Ridge regression reduces the effect of problematic variables close to zero but never fully removes them.

We will go through an example of ridge regression using the VietNamI dataset available in the pydataset library. Our goal will be to predict expenses based on the variables available. We will complete this task using the following steps/

  1. Data preparation
  2. Baseline model development
  3. Ridge regression model

Below is the initial code

from pydataset import data
import numpy as np
import pandas as pd
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Ridge
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_erro

Data Preparation

The data preparation is simple. All we have to do is load the data and convert the sex variable to a dummy variable. We also need to set up our X and y datasets. Below is the code.

df=pd.DataFrame(data('VietNamI'))
df.loc[df.sex== 'male', 'sex'] = 0
df.loc[df.sex== 'female','sex'] = 1
df['sex'] = df['sex'].astype(int)
X=df[['pharvis','age','sex','married','educ','illness','injury','illdays','actdays','insurance']]
y=df['lnhhexp'

We can now create our baseline regression model.

Baseline Model

The metric we are using is the mean squared error. Below is the code and output for our baseline regression model. This is a model that has no regularization to it. Below is the code.

regression=LinearRegression()
regression.fit(X,y)
first_model=(mean_squared_error(y_true=y,y_pred=regression.predict(X)))
print(first_model)
0.35528915032173053

This  value of 0.355289 will be our indicator to determine if the regularized ridge regression model is superior or not.

Ridge Model

In order to create our ridge model we need to first determine the most appropriate value for the l2 regularization. L2 is the name of the hyperparameter that is used in ridge regression. Determining the value of a hyperparameter requires the use of a grid. In the code below, we first are ridge model and indicate normalization in order to get better estimates. Next we setup the grid that we will use. Below is the code.

ridge=Ridge(normalize=True)
search=GridSearchCV(estimator=ridge,param_grid={'alpha':np.logspace(-5,2,8)},scoring='neg_mean_squared_error',n_jobs=1,refit=True,cv=10)

The search object has several arguments within it. Alpha is hyperparameter we are trying to set. The log space is the range of values we want to test. We want the log of -5 to 2, but we only get 8 values from within that range evenly spread out. Are metric is the mean squared error. Refit set true means to adjust the parameters while modeling and cv is the number of folds to develop for the cross-validation. We can now use the .fit function to run the model and then use the .best_params_ and .best_scores_ function to determine the model;s strength. Below is the code.

search.fit(X,y)
search.best_params_
{'alpha': 0.01}
abs(search.best_score_)
0.3801489007094425

The best_params_ tells us what to set alpha too which in this case is 0.01. The best_score_ tells us what the best possible mean squared error is. In this case, the value of 0.38 is worse than what the baseline model was. We can confirm this by  fitting our model with the ridge information and finding the mean squared error. This is done below.

ridge=Ridge(normalize=True,alpha=0.01)
ridge.fit(X,y)
second_model=(mean_squared_error(y_true=y,y_pred=ridge.predict(X)))
print(second_model)
0.35529321992606566

The 0.35 is lower than the 0.38. This is because the last results are not cross-validated. In addition, these results indicate that there is little difference between the ridge and baseline models. This is confirmed with the coefficients of each model found below.

coef_dict_baseline = {}
for coef, feat in zip(regression.coef_,data("VietNamI").columns):
coef_dict_baseline[feat] = coef
coef_dict_baseline
Out[188]:
{'pharvis': 0.013282050886950674,
'lnhhexp': 0.06480086550467873,
'age': 0.004012412278795848,
'sex': -0.08739614349708981,
'married': 0.075276463838362,
'educ': -0.06180921300600292,
'illness': 0.040870384578962596,
'injury': -0.002763768716569026,
'illdays': -0.006717063310893158,
'actdays': 0.1468784364977112}


coef_dict_ridge = {}
for coef, feat in zip(ridge.coef_,data("VietNamI").columns):
coef_dict_ridge[feat] = coef
coef_dict_ridge
Out[190]:
{'pharvis': 0.012881937698185289,
'lnhhexp': 0.06335455237380987,
'age': 0.003896623321297935,
'sex': -0.0846541637961565,
'married': 0.07451889604357693,
'educ': -0.06098723778992694,
'illness': 0.039430607922053884,
'injury': -0.002779341753010467,
'illdays': -0.006551280792122459,
'actdays': 0.14663287713359757}

The coefficient values are about the same. This means that the penalization made little difference with this dataset.

Conclusion

Ridge regression allows you to penalize variables based on their useful in developing the model. With this form of regularized regression the coefficients of the variables is never set to zero. Other forms of regularization regression allows for the total removal of variables. One example of this is lasso regression.

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Hyperparameter Tuning in Python

Hyperparameters are a numerical quantity you must set yourself when developing a model. This is often one of the last steps of model development. Choosing an algorithm and determining which variables to include often come before this step.

Algorithms cannot determine hyperparameters themselves which is why you have to do it. The problem is that the typical person has no idea what is an optimally choice for the hyperparameter. To deal with this confusion, often a range of values are inputted and then it is left to python to determine which combination of hyperparameters is most appropriate.

In this post, we will learn how to set hyperparameters by developing a grid in  Python. To do this, we will use the PSID dataset from the pydataset library. Our goal will be to classify who is married and not married based on several independent variables. The steps of this process is as follows

  1.  Data preparation
  2. Baseline model (for comparison)
  3. Grid development
  4. Revised model

Below is some initial code that includes all the libraries and classes that we need.

import pandas as pd
import numpy as np
from pydataset import data
pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 5000)
pd.set_option('display.width', 10000)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV

Data Preparation

The dataset PSID has several problems that we need to address.

  • We need to remove all NAs
  • The married variable will be converted to a dummy variable. It will simply be changed to married or not rather than all of the other possible categories.
  • The educatnn and kids variables have codes that are 98 and 99. These need to be removed because they do not  make sense.

Below is the code that deals with all of this

df=data('PSID').dropna()
df.loc[df.married!= 'married', 'married'] = 0
df.loc[df.married== 'married','married'] = 1
df['married'] = df['married'].astype(int)
df['marry']=df.married
df.drop(df.loc[df['kids']>90].index, inplace=True)
df.drop(df.loc[df['educatn']>90].index, inplace=True
  1. Line 1 loads the dataset and drops the NAs
  2. Line 2-4 create our dummy variable for marriage. We create a new variable called marry to hold the results
  3. Lines 5-6 drop the values in  kids and educatn that are above 90.

Below we create our X and y datasets and then are ready to make our baseline model.

X=df[['age','educatn','hours','kids','earnings']]
y=df['marry']

Baseline Model

The purpose of  baseline model is to see how much better or worst the hyperparameter tuning works. We are using K Nearest Neighbors  for our classification. In our example, there are 4 hyperparameters we need to set. They are as follows.

  1. number of neighbors
  2. weight of neighbors
  3. metric for measuring distance
  4. power parameter for minkowski

Below is the baseline model with the set hyperparameters. The second line shows the accuracy of the model after a k-fold cross-validation that was set to 10.

classifier=KNeighborsClassifier(n_neighbors=5,weights=’uniform’, metric=’minkowski’,p=2)
np.mean(cross_val_score(classifier,X,y,cv=10,scoring=’accuracy’,n_jobs=1)) 0.6188104238047426

Our model has an accuracy of about 62%. We will now move to setting up our grid so we can see if tuning the hyperparameters improves the performance

Grid Development

The grid allows you to develop scores of models with all of the hyperparameters tuned slightly differently. In the code below, we create our grid object, and then we calculate how many models we will run

grid={'n_neighbors':range(1,13),'weights':['uniform','distance'],'metric':['manhattan','minkowski'],'p':[1,2]}
np.prod([len(grid[element]) for element in grid])
96

You can see we made a simple list that has several values for each hyperparameter

  1. Number if neighbors can be 1 to 13
  2. weight of neighbors can be uniform or distance
  3. metric can be manhatten or minkowski
  4. p can be 1 or 2

We will develop 96 models all together. Below is the code to begin tuning the hyperparameters.

search=GridSearchCV(estimator=classifier,param_grid=grid,scoring='accuracy',n_jobs=1,refit=True,cv=10)
search.fit(X,y)

The estimator is the  code for the type of algorithm we are using. We set this earlier. The param_grid is our grid. Accuracy is our metric for determining the best model. n_jobs has to do with the amount of resources committed to the process. refit is for changing parameters and cv is for cross-validation folds.The search.fit command runs the model

The code below provides the output for the results.

print(search.best_params_)
print(search.best_score_)
{'metric': 'manhattan', 'n_neighbors': 11, 'p': 1, 'weights': 'uniform'}
0.6503975265017667

The best_params_ function tells us what the most appropriate parameters are. The best_score_ tells us what the accuracy of the model is with the best parameters. Are model accuracy improves from 61% to 65% from adjusting the hyperparameters. We can confirm this by running our revised model with the updated hyper parameters.

Model Revision

Below is the cod efor the erevised model

classifier2=KNeighborsClassifier(n_neighbors=11,weights='uniform', metric='manhattan',p=1)
np.mean(cross_val_score(classifier2,X,y,cv=10,scoring='accuracy',n_jobs=1)) #new res
Out[24]: 0.6503909993913031

Exactly as we thought. This is a small improvement but this can make a big difference in some situation such as in a data science competition.

Conclusion

Tuning hyperparameters is one of the final pieces to improving a model. With this tool, small gradually changes can be seen in a model. It is important to keep in mind this aspect of model development in order to have the best success final.

Variable Selection in Python

A key concept in machine learning and data science in general is variable selection. Sometimes, a dataset can have hundreds of variables to include in a model. The benefit of variable selection is that it reduces the amount of useless information aka noise in the model. By removing noise it can improve the learning process and help to stabilize the estimates.

In this post, we will look at two ways to do this.  These two common approaches are the univariate approach and the greedy approach. The univariate approach selects variables that are most related to the dependent variable based on a metric. The greedy approach will alone remove a variable if getting rid of it does not affect the model’s performance.

We will now move to our first example which is the univariate approach using Python. We will use the VietNamH dataset from the pydataset library. Are goal is to predict how much a family spends on medical expenses. Below is the initial code.

import pandas as pd
import numpy as np
from pydataset import data
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import SelectPercentile
from sklearn.feature_selection import f_regression
df=data('VietNamH').dropna()

Are data is called df. If you use the head function, you will see that we need to convert several variables to dummy variables. Below is the code for doing this.

df.loc[df.sex== 'female', 'sex'] = 0
df.loc[df.sex== 'male','sex'] = 1
df.loc[df.farm== 'no', 'farm'] = 0
df.loc[df.farm== 'yes','farm'] = 1
df.loc[df.urban== 'no', 'urban'] = 0
df.loc[df.urban== 'yes','urban'] = 1

We now need to setup or X and y datasets as shown below

X=df[['age','educyr','sex','hhsize','farm','urban','lnrlfood']]
y=df['lnmed']

We are now ready to actual use the univariate approach. This involves the use of two different classes in Python. The SelectPercentile class allows you to only include the variables that meet a certain percentile rank such as 25%. The f_regression class is designed for checking a variable’s performance in the context of regression.  Below is the code to run the analysis.

selector_f=SelectPercentile(f_regression,percentile=25)
selector_f.fit(X,y)

We can now see the results using a for loop. We want the scores from our selector_f object. To do this we setup a for lop and use the zip function to iterate over the data. The output is placed in the print statement. Below is the code and output for this.

for n,s in zip(X,selector_f.scores_):
print('F-score: %3.2f\t for feature %s ' % (s,n))
F-score: 62.42 for feature age
F-score: 33.86 for feature educyr
F-score: 3.17 for feature sex
F-score: 106.35 for feature hhsize
F-score: 14.82 for feature farm
F-score: 5.95 for feature urban
F-score: 97.77 for feature lnrlfood

You can see the f-score for all of the independent variables. You can decide for yourself which to include.

Greedy Approach

The greedy approach only removes variables if they do not impact model performance. We are using the same dataset so all we have to do is run the code. We need the RFECV class from the model_selection library. We then use the function RFECV and set the estimator, cross-validation, and scoring metric. Finally, we run the analysis and print the results. The code is below with the output.

from sklearn.feature_selection import RFECV
select=RFECV(estimator=regression,cv=10,scoring='neg_mean_squared_error')
select.fit(X,y)
print(select.n_features_)
7

The number 7 represents how many independent variables to include in the model. Since we only had 7 total variables we should include all variables in the model.

Conclusion

With help with univariate and greedy approaches, it is possible to deal with a large number of variables efficiently one developing models. The example here involve only a handful of variables. However, bear in mind that the approaches mentioned here are highly scalable and useful.

Scatter Plots in Python

Scatterplots are one of many crucial forms of visualization in statistics. With scatterplots, you can examine the relationship between two variables. This can lead to insights in terms of decision making or additional analysis.

We will be using the “Prestige” dataset form the pydataset module to look at scatterplot use. Below is some initial code.

from pydataset import data
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df=data('Prestige')

We will begin by making a correlation matrix. this will help us to determine which pairs of variables have strong relationships with each other. This will be done with the .corr() function. below is the code

1.png

You can see that there are several strong relationships. For our purposes, we will look at the relationship between education and income.

The seaborn library is rather easy to use for making visuals. To make a plot you can use the .lmplot() function. Below is a basic scatterplot of our data.

1.png

The code should be self-explanatory. THe only thing that might be unknown is the fit_reg argument. This is set to False so that the function does not make a regression line. Below is the same visual but this time with the regression line.

facet = sns.lmplot(data=df, x='education', y='income',fit_reg=True)

1

It is also possible to add a third variable to our plot. One of the more common ways is through including a categorical variable. Therefore, we will look at job type and see what the relationship is. To do this we use the same .lmplot.() function but include several additional arguments. These include the hue and the indication of a legend. Below is the code and output.

1.png

You can clearly see that type separates education and income. A look at the boxplots for these variables confirms this.

1.png

As you can see, we can conclude that job type influences both education and income in this example.

Conclusion

This post focused primarily on making scatterplots with the seaborn package. Scatterplots are a tool that all data analyst should be familiar with as it can be used to communicate information to people who must make decisions.

Data Visualization in Python

In this post, we will look at how to set up various features of a graph in Python. The fine tune tweaks that can be made when creating a data visualization can be enhanced the communication of results with an audience. This will all be done using the matplotlib module available for python. Our objectives are as follows

  • Make a graph with  two lines
  • Set the tick marks
  • Change the linewidth
  • Change the line color
  • Change the shape of the line
  • Add a label to each axes
  • Annotate the graph
  • Add a legend and title

We will use two variables from the “toothpaste” dataset from the pydataset module for this demonstration. Below is some initial code.

from pydataset import data
import matplotlib.pyplot as plt
DF = data('toothpaste')

Make Graph with Two Lines

To make a plot you use the .plot() function. Inside the parentheses you out the dataframe and variable you want. If you want more than one line or graph you use the .plot() function several times. Below is the code for making a graph with two line plots using variables from the toothpaste dataset.

plt.plot(DF['meanA'])
plt.plot(DF['sdA'])

1

To get the graph above you must run both lines of code simultaneously. Otherwise, you will get two separate graphs.

Set Tick Marks

Setting the tick marks requires the use of the .axes() function. However, it is common to save this function in a variable called axes as a handle. This makes coding easier. Once this is done you can use the .set_xticks() function for the x-axes and .set_yticks() for the y axes. In our example below, we are setting the tick marks for the odd numbers only. Below is the code.

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.plot(DF['meanA'])
plt.plot(DF['sdA'])

1

Changing the Line Type

It is also possible to change the line type and width. There are several options for the line type. The important thing here is to put this information after the data you want to plot inside the code. Line width is changed with an argument that has the same name. Below is the code and visual

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.plot(DF['meanA'],'--',linewidth=3)
plt.plot(DF['sdA'],':',linewidth=3)

1

Changing the Line Color

It is also possible to change the line color. There are several options available. The important thing is that the argument for the line color goes inside the same parentheses as the line type. Below is the code. r means red and k means black.

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.plot(DF['meanA'],'r--',linewidth=3)
plt.plot(DF['sdA'],'k:',linewidth=3)

1

Change the Point Type

Changing the point type requires more code inside the same quotation marks where the line color and line type are. Again there are several choices here. The code is below

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.plot(DF['meanA'],'or--',linewidth=3)
plt.plot(DF['sdA'],'Dk:',linewidth=3)

1

Add X and Y Labels

Adding LAbels is simple. You just use the .xlabel() function or .ylabel() function. Inside the parentheses, you put the text you want in quotation marks. Below is the code.

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.xlabel('X Example')
plt.ylabel('Y Example')
plt.plot(DF['meanA'],'or--',linewidth=3)
plt.plot(DF['sdA'],'Dk:',linewidth=3)

1

Adding Annotation, Legend, and Title

Annotation allows you to write text directly inside the plot wherever you want. This involves the use of the .annotate function. Inside this function, you must indicate the location of the text and the actual text you want added to the plot. For our example, we will add the word ‘python’ to the plot for fun.

The .legend() function allows you to give a description of the line types that you have included. Lastly, the .title() function allows you to add a title. Below is the code.

ax=plt.axes()
ax.set_xticks([1,3,5,7,9])
ax.set_yticks([1,3,5,7,9])
plt.xlabel('X Example')
plt.ylabel('Y Example')
plt.annotate(xy=[3,4],text='Python')
plt.plot(DF['meanA'],'or--',linewidth=3)
plt.plot(DF['sdA'],'Dk:',linewidth=3)
plt.legend(['1st','2nd'])
plt.title("Plot Example")

1.png

Conclusion

Now you have a practical understanding of how you can communicate information visually with matplotlib in python. This is barely scratching the surface in terms of the potential that is available.

Random Forest Regression in Python

Random forest is simply the making of dozens if not thousands of decision trees. The decision each tree makes about an example are then tallied for the purpose of voting with the classification that receives the most votes winning. For regression, the results of the trees are averaged in  order to give the most accurate results

In this post, we will use the cancer dataset from the pydataset module to predict the age of people. Below is some initial code.

import pandas as pd
import numpy as np
from pydataset import data
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

We can load our dataset as df, drop all NAs, and create our dataset that contains the independent variables and a separate dataset that includes the dependent variable of age. The code is below

df = data('cancer')
df=df.dropna()
X=df[['time','status',"sex","ph.ecog",'ph.karno','pat.karno','meal.cal','wt.loss']]
y=df['age']

Next, we need to set up our train and test sets using a 70/30 split. After that, we set up our model using the RandomForestRegressor function. n_estimators is the number of trees we want to create and the random_state argument is for supporting reproducibility. The code is below

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
h=RandomForestRegressor(n_estimators=100,random_state=1)

We can now run our model and test it. Running the model requires the .fit() function and testing involves the .predict() function. The results of the test are found using the mean_squared_error() function.

h.fit(x_train,y_train)
y_pred=h.predict(x_test)
mean_squared_error(y_test,y_pred)
71.75780196078432

The MSE of 71.75 is only useful for model comparison and has little meaning by its self. Another way to assess the model is by determining variable importance. This helps you to determine in a descriptive way the strongest variables for the regression model. The code is below followed by the plot of the variables.

model_ranks=pd.Series(h.feature_importances_,index=x_train.columns,name="Importance").sort_values(ascending=True,inplace=False) 
ax=model_ranks.plot(kind='barh')

1

As you can see, the strongest predictors of age include calories per meal, weight loss, and time sick. Sex and whether the person is censored or dead make a smaller difference. This makes sense as younger people eat more and probably lose more weight because they are heavier initially when dealing with cancer.

Conclusison

This post provided an example of the use of regression with random forest. Through the use of ensemble voting, you can improve the accuracy of your models. This is a distinct power that is not available with other machine learning algorithm.

Bagging Classification with Python

Bootstrap aggregation aka bagging is a technique used in machine learning that relies on resampling from the sample and running multiple models from the different samples. The mean or some other value is calculated from the results of each model. For example, if you are using Decisions trees, bagging would have you run the model several times with several different subsamples to help deal with variance in statistics.

Bagging is an excellent tool for algorithms that are considered weaker or more susceptible to variances such as decision trees or KNN. In this post, we will use bagging to develop a model that determines whether or not people voted using the turnout dataset. These results will then be compared to a model that was developed in a traditional way.

We will use the turnout dataset available in the pydataset module. Below is some initial code.

from pydataset import data
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report

We will load our dataset. Then we will separate the independnet and dependent variables from each other and create our train and test sets. The code is below.

df=data("turnout")
X=df[['age','educate','income',]]
y=df['vote']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)

We can now prepare to run our model. We need to first set up the bagging function. There are several arguments that need to be set. The max_samples argument determines the largest amount of the dataset to use in resampling. The max_features argument is the max number of features to use in a sample. Lastly, the n_estimators is for determining the number of subsamples to draw. The code is as follows

 h=BaggingClassifier(KNeighborsClassifier(n_neighbors=7),max_samples=0.7,max_features=0.7,n_estimators=1000)

Basically, what we told python was to use up to 70% of the samples, 70% of the features, and make 100 different KNN models that use seven neighbors to classify. Now we run the model with the fit function, make a prediction with the predict function, and check the accuracy with the classificarion_reoirts function.

h.fit(X_train,y_train)
y_pred=h.predict(X_test)
print(classification_report(y_test,y_pred))

1

This looks oka below are the results when you do a traditional model without bagging

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)
clf=KNeighborsClassifier(7)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print(classification_report(y_test,y_pred))

1

The improvement is not much. However, this depends on the purpose and scale of your project. A small improvement can mean millions in the reight context such as for large companies such as Google who deal with billions of people per day.

Conclusion

This post provides an example of the use of bagging in the context of classification. Bagging provides a why to improve your model through the use of resampling.

K Nearest Neighbor Classification with Python

K Nearest Neighbor uses the idea of proximity to predict class. What this means is that with KNN Python will look at K neighbors to determine what the unknown examples class should be. It is your job to determine the K or number of neighbors that should be used to determine the unlabeled examples class.

KNN is great for a small dataset. However, it normally does not scale well when the dataset gets larger and larger. As such, unless you have an exceptionally powerful computer KNN is probably not going to do well in a Big Data context.

In this post, we will go through an example of the use of KNN with the turnout dataset from the pydataset module. We want to predict whether someone voted or not based on the independent variables. Below is some initial code.

from pydataset import data
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import classification_report

We now need to load the data and separate the independent variables from the dependent variable by making two datasets.

df=data("turnout")
X=df[['age','educate','income']]
y=df['vote']

Next, we will make our train and test sets with a 70/30 split. The random.state is set to 0. This argument allows us to reproduce our model if we want. After this, we will run our model. We will set the K to 7 for our model  and run the model. This means that Python will look at the 7 closes examples to predict the value of an unknown example. below is the code

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)
clf=KNeighborsClassifier(7)
clf.fit(X_train,y_train)

We can now predict with our model and see the results with the classification reports function.

y_pred=clf.predict(X_test)
print(classification_report(y_test,y_pred))

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The results are shown above. To determine the quality of the model relies more on domain knowledge. What we can say for now is that the model is better at classifying people who vote rather than people who do not vote.

Conclusion

This post shows you how to work with Python when using KNN. This algorithm is useful in using neighboring examples tot predict the class of an unknown example.

Naive Bayes with Python

Naive Bayes is a probabilistic classifier that is often employed when you have multiple or more than two classes in which you want to place your data. This algorithm is particularly used when you dealing with text classification with large datasets and many features.

If you are more familiar with statistics you know that Bayes developed a method of probability that is highly influential today. In short, his system takes into conditional probability. In the case of naive Bayes,  the classifier assumes that the presence of a certain feature in a class is not related to the presence of any other feature. This assumption is why Naive Bayes is Naive.

For our purposes, we will use Naive Bayes to predict the type of insurance a person has in the DoctorAUS dataset in the pydataset module. Below is some initial code.

from pydataset import data
import pandas as pd
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Next, we will load our dataset DoctorAUS. Then we will separate the independent variables that we will use from the dependent variable of insurance in two different datasets. If you want to know more about the dataset and the variables you can type data(“DoctorAUS”, show_doc=True)

df=data("DoctorAUS")
X=df[['age','income','sex','illness','actdays','hscore','doctorco','nondocco','hospadmi','hospdays','medecine','prescrib']]
y=df['insurance']

Now, we will create our train and test datasets. We will do a 70/30 split. We will also use Gaussian Naive Bayes as our algorithm. This algorithm assumes the data is normally distributed. There are other algorithms available for Naive Bayes as well.  We will also create our model with the .fit function.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)
clf=GaussianNB()
clf.fit(X_train,y_train)

Finally, we will predict with our model and run the classification report to determine the success of the model.

y_pred=clf.predict(X_test)
print(classification_report(y_test,y_pred))

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You can see that our overall numbers are not that great. This means that the current algorithm is probably not the best choice for classification. Of course, there could other problems as well that need to be explored.

Conclusion

This post was simply a demonstration of how to conduct an analysis with Naive Bayes using Python. The process is not all that complicate and is similar to other algorithms that are used.

K Nearest Neighbor Regression with Python

K Nearest Neighbor Regression (KNN) works in much the same way as KNN for classification. The difference lies in the characteristics of the dependent variable. With classification KNN the dependent variable is categorical. WIth regression KNN the dependent variable is continuous. Both involve the use neighboring examples to predict the class or value of other examples.

This post will provide an example of KNN regression using the turnout dataset from the pydataset module. Our purpose will be to predict the age of a voter through the use of other variables in the dataset. Below is some initial code.

from pydataset import data
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error

We now need to setup our data. We need to upload our actual dataset. Then we need to separate the independnet and dependent variables. Once this is done we need to create our train and test sets using the tarin test spli t funvtion. Below is the code to accmplouh each of these steps.

df=data("turnout")
X=df[['age','income','vote']]
y=df['educate']
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)

We are now ready to train our model. We need to call the function we will use and determine the size of K, which will be 11 in our case. Then we need to train our model and then predict with it. Lastly, we will print out the mean squared error. This value is useful for comparing models but does not have much value by itself. The MSE is calculated by comparing the actual test set with the predicted test data. The code is below

clf=KNeighborsRegressor(11)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
print(mean_squared_error(y_test,y_pred))
9.239

If we were to continue with model development we may look for ways to improve our MAE through different nethods such as regular linear regression. However, for our purposes this is adequate.

Conclusison

This post provides an example of regression with KNN in Python. This tool is a practical and simple way to make numeric predictions that can be accurate at times.

Support Vector Machines Regression with Python

This post will provide an example of how to do regression with support vector machines SVM. SVM is a complex algorithm that allows for the development of non-linear models. This is particularly useful for messy data that does not have clear boundaries.

The steps that we will use are listed below

  1. Data preparation
  2. Model Development

We will use two different kernels in our analysis. The LinearSVR kernel and SVR kernel. The difference between these two kernels has to do with slight changes in the calculations of the boundaries between classes.

Data Preparation

We are going to use the OFP dataset available in the pydataset module. This dataset was used previously for classification with SVM on this site. Our plan this time is that we want to predict family inc (famlinc), which is a continuous variable.  Below is some initial code.

import numpy as np
import pandas as pd
from pydataset import data
from sklearn import svm
from sklearn import model_selection
from statsmodels.tools.eval_measures import mse

We now need to load our dataset and remove any missing values.

df=pd.DataFrame(data('OFP'))
df=df.dropna()

AS in the previous post, we need to change the text variables into dummy variables and we also need to scale the data. The code below creates the dummy variables, removes variables that are not needed, and also scales the data.

dummy=pd.get_dummies(df['black'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "black_person"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['sex'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"male": "Male"})
df=df.drop('female', axis=1)

dummy=pd.get_dummies(df['employed'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "job"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['maried'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"no": "single"})
df=df.drop('yes', axis=1)

dummy=pd.get_dummies(df['privins'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "insured"})
df=df.drop('no', axis=1)
df=df.drop(['black','sex','maried','employed','privins','medicaid','region','hlth'],axis=1)
df = (df - df.min()) / (df.max() - df.min())
df.head()

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We now need to set up our datasets. The X dataset will contain the independent variables while the y dataset will contain the dependent variable

X=df[['ofp','ofnp','opp','opnp','emr','hosp','numchron','adldiff','age','school','single','black_person','Male','job','insured']]
y=df['faminc']

We can now move to model development

Model Development

We now need to create our train and test sets for or X and y datasets. We will do a 70/30 split of the data. Below is the code

X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=.3,random_state=1)

Next, we will create our two models with the code below.

h1=svm.SVR()
h2=svm.LinearSVR()

We will now run our first model and assess the results. Our metric is the mean squared error. Generally, the lower the number the better.  We will use the .fit() function to train the model and the .predict() function for test the model

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The mse was 0.27. This number means nothing only and is only beneficial for comparison reasons. Therefore, the second model will be judged as better or worst only if the mse is lower than 0.27. Below are the results of the second model.

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We can see that the mse for our second model is 0.34 which is greater than the mse for the first model. This indicates that the first model is superior based on the current results and parameter settings.

Conclusion

This post provided an example of how to use SVM for regression.

Support Vector Machines Classification with Python

Support vector machines (SVM) is an algorithm used to fit non-linear models. The details are complex but to put it simply  SVM tries to create the largest boundaries possible between the various groups it identifies in the sample. The mathematics behind this is complex especially if you are unaware of what a vector is as defined in algebra.

This post will provide an example of SVM using Python broken into the following steps.

  1. Data preparation
  2. Model Development

We will use two different kernels in our analysis. The linear kernel and he rbf kernel. The difference in terms of kernels has to do with how the boundaries between the different groups are made.

Data Preparation

We are going to use the OFP dataset available in the pydataset module. We want to predict if someone single or not. Below is some initial code.

import numpy as np
import pandas as pd
from pydataset import data
from sklearn import svm
from sklearn.metrics import classification_report
from sklearn import model_selection

We now need to load our dataset and remove any missing values.

df=pd.DataFrame(data('OFP'))
df=df.dropna()
df.head()

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Looking at the dataset we need to do something with the variables that have text. We will create dummy variables for all except region and hlth. The code is below.

dummy=pd.get_dummies(df['black'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "black_person"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['sex'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"male": "Male"})
df=df.drop('female', axis=1)

dummy=pd.get_dummies(df['employed'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "job"})
df=df.drop('no', axis=1)

dummy=pd.get_dummies(df['maried'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"no": "single"})
df=df.drop('yes', axis=1)

dummy=pd.get_dummies(df['privins'])
df=pd.concat([df,dummy],axis=1)
df=df.rename(index=str, columns={"yes": "insured"})
df=df.drop('no', axis=1)

For each variable, we did the following

  1. Created a dummy in the dummy dataset
  2. Combined the dummy variable with our df dataset
  3. Renamed the dummy variable based on yes or no
  4. Drop the other dummy variable from the dataset. Python creates two dummies instead of one.

If you look at the dataset now you will see a lot of variables that are not necessary. Below is the code to remove the information we do not need.

df=df.drop(['black','sex','maried','employed','privins','medicaid','region','hlth'],axis=1)
df.head()

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This is much cleaner. Now we need to scale the data. This is because SVM is sensitive to scale. The code for doing this is below.

df = (df - df.min()) / (df.max() - df.min())
df.head()

1

We can now create our dataset with the independent variables and a separate dataset with our dependent variable. The code is as follows.

X=df[['ofp','ofnp','opp','opnp','emr','hosp','numchron','adldiff','age','school','faminc','black_person','Male','job','insured']]
y=df['single']

We can now move to model development

Model Development

We need to make our test and train sets first. We will use a 70/30 split.

X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=.3,random_state=1)

Now, we need to create the models or the hypothesis we want to test. We will create two hypotheses. The first model is using a linear kernel and the second is one using the rbf kernel. For each of these kernels, there are hyperparameters that need to be set which you will see in the code below.

h1=svm.LinearSVC(C=1)
h2=svm.SVC(kernel='rbf',degree=3,gamma=0.001,C=1.0)

The details about the hyperparameters are beyond the scope of this post. Below are the results for the first model.

1.png

The overall accuracy is 73%. The crosstab() function provides a breakdown of the results and the classification_report() function provides other metrics related to classification. In this situation, 0 means not single or married while 1 means single. Below are the results for model 2

1.png

You can see the results are similar with the first model having a slight edge. The second model really struggls with predicting people who are actually single. You can see thtat the recall in particular is really poor.

Conclusion

This post provided how to ob using SVM in python. How this algorithm works can be somewhat confusing. However, its use can be powerful if use appropriately.

Linear Discriminant Analysis in Python

Linear discriminant analysis is a classification algorithm commonly used in data science. In this post, we will learn how to use LDA with Python. The steps we will for this are as follows.

  1. Data preparation
  2. Model training and evaluation

Data Preparation

We will be using the bioChemists dataset which comes from the pydataset module. We want to predict whether someone is married or single based on academic output and prestige. Below is some initial code.

import pandas as pd
from pydataset import data
import matplotlib.pyplot as plt
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Now we will load our data and take a quick look at it using the .head() function.

1.png

There are two variables that contain text so we need to convert these two dummy variables for our analysis the code is below with the output.

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Here is what we did.

  1. We created the dummy variable by using the .get_dummies() function.
  2. We saved the output in an object called dummy
  3. We then combine the dummy and df dataset with the .concat() function
  4. We repeat this process for the second variable

The output shows that we have our original variables and the dummy variables. However, we do not need all of this information. Therefore, we will create a dataset that has the X variables we will use and a separate dataset that will have our y values. Below is the code.

X=df[['Men','kid5','phd','ment','art']]
y=df['Married']

The X dataset has our five independent variables and the y dataset has our dependent variable which is married or not. We can not split our data into a train and test set.  The code is below.

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=.3,random_state=0)

The data was split 70% for training and 30% for testing. We made a train and test set for the independent and dependent variables which meant we made 4 sets altogether. We can now proceed to model development and testing

Model Training and Testing

Below is the code to run our LDA model. We will use the .fit() function for this.

clf=LDA()
clf.fit(X_train,y_train)

We will now use this model to predict using the .predict function

y_pred=clf.predict(X_test)

Now for the results, we will use the classification_report function to get all of the metrics associated with a confusion matrix.

1.png

The interpretation of this information is described in another place. For our purposes, we have an accuracy of 71% for our prediction.  Below is a visual of our model using the ROC curve.

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Here is what we did

  1. We had to calculate the roc_curve for the model this is explained in detail here
  2. Next, we plotted our own curve and compared to a baseline curve which is the dotted lines.

A ROC curve of 0.67 is considered fair by many. Our classification model is not that great but there are worst models out there.

Conclusion

This post went through an example of developing and evaluating a linear discriminant model. To do this you need to prepare the data, train the model, and evaluate.

Factor Analysis in Python

Factor analysis is a dimensionality reduction technique commonly used in statistics. FA is similar to principal component analysis. The difference are highly technical but include the fact the FA does not have an orthogonal decomposition and FA assumes that there are latent variables and that are influencing the observed variables in the model. For FA the goal is the explanation of the covariance among the observed variables present.

Our purpose here will be to use the BioChemist dataset from the pydataset module and perform a FA that creates two components. This dataset has data on the people completing PhDs and their mentors. We will also create a visual of our two-factor solution. Below is some initial code.

import pandas as pd
from pydataset import data
from sklearn.decomposition import FactorAnalysis
import matplotlib.pyplot as plt

We now need to prepare the dataset. The code is below

df = data('bioChemists')
df=df.iloc[1:250]
X=df[['art','kid5','phd','ment']]

In the code above, we did the following

  1. The first line creates our dataframe called “df” and is made up of the dataset bioChemist
  2. The second line reduces the df to 250 rows. This is done for the visual that we will make. To take the whole dataset and graph it would make a giant blob of color that would be hard to interpret.
  3. The last line pulls the variables we want to use for our analysis. The meaning of these variables can be found by typing data(“bioChemists”,show_doc=True)

In the code below we need to set the number of factors we want and then run the model.

fact_2c=FactorAnalysis(n_components=2)
X_factor=fact_2c.fit_transform(X)

The first line tells Python how many factors we want. The second line takes this information along with or revised dataset X to create the actual factors that we want. We can now make our visualization

To make the visualization requires several steps. We want to identify how well the two components separate students who are married from students who are not married. First, we need to make a dictionary that can be used to convert the single or married status to a number. Below is the code.

thisdict = {
"Single": "1",
"Married": "2",}

Now we are ready to make our plot. The code is below. Pay close attention to the ‘c’ argument as it uses our dictionary.

plt.scatter(X_factor[:,0],X_factor[:,1],c=df.mar.map(thisdict),alpha=.8,edgecolors='none')

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You can perhaps tell why we created the dictionary now. By mapping the dictionary to the mar variable it automatically changed every single and married entry in the df dataset to a 1 or 2. The c argument needs a number in order to set a color and this is what the dictionary was able to supply it with.

You can see that two factors do not do a good job of separating the people by their marital status. Additional factors may be useful but after two factors it becomes impossible to visualize them.

Conclusion

This post provided an example of factor analysis in Python. Here the focus was primarily on visualization but there are so many other ways in which factor analysis can be deployed.

Analyzing Twitter Data in Python

In this post, we will look at how to analyze text from Twitter. We will do each of the following for tweets that refer to Donald Trump and tweets that refer to Barrack Obama.

  • Conduct a sentiment analysis
  • Create a word cloud

This is a somewhat complex analysis so I am assuming that you are familiar with Python as explaining everything would make the post much too long. In order to achieve our two objectives above we need to do the following.

  1. Obtain all of the necessary information from your twitter apps account
  2. Download the tweets & clean
  3. Perform the analysis

Before we begin, here is a list of modules we will need to load to complete our analysis

import wordcloud
import matplotlib.pyplot as plt
import twython
import re
import numpy

Obtain all Needed Information

From your twitter app account, you need the following information

  • App key
  • App key secret
  • Access token
  • Access token secret

All this information needs to be stored in individual objects in Python. Then each individual object needs to be combined into one object. The code is below.

TWITTER_APP_KEY=XXXXXXXXXXXXXXXXXXXXXXXXXX
TWITTER_APP_KEY_SECRET=XXXXXXXXXXXXXXXXXXX
TWITTER_ACCESS_TOKEN=XXXXXXXXXXXXXXXXXXXXX
TWITTER_ACCESS_TOKEN_SECRET=XXXXXXXXXXXXXX
t=twython.Twython(app_key=TWITTER_APP_KEY,app_secret=TWITTER_APP_KEY_SECRET,oauth_token=TWITTER_ACCESS_TOKEN,oauth_token_secret=TWITTER_ACCESS_TOKEN_SECRET)

In the code above we saved all the information in different objects at first and then combined them. You will of course replace the XXXXXXX with your own information.

Next, we need to create a function that will pull the tweets from Twitter. Below is the code,

def get_tweets(twython_object,query,n):
   count=0
   result_generator=twython_object.cursor(twython_object.search,q=query)

   result_set=[]
   for r in result_generator:
      result_set.append(r['text'])
      count+=1
      if count ==n: break

   return result_set

You will have to figure out the code yourself. We can now download the tweets.

Downloading Tweets & Clean

Downloading the tweets involves making an empty dictionary that we can save our information in. We need two keys in our dictionary one for Trump and the other for Obama because we are downloading tweets about these two people.

There are also two additional things we need to do. We need to use regular expressions to get rid of punctuation and we also need to lower case all words. All this is done in the code below.

tweets={}
tweets['trump']=[re.sub(r'[-.#/?!.":;()\']',' ',tweet.lower())for tweet in get_tweets(t,'#trump',1500)]
tweets['obama']=[re.sub(r'[-.#/?!.":;()\']',' ',tweet.lower())for tweet in get_tweets(t,'#obama',1500)]

The get_tweets function is also used in the code above along with our twitter app information. We pulled 1500 tweets concerning Obama and 1500 tweets about Trump. We were able to download and clean our tweets at the same time. We can now do our analysis

Analysis

To do the sentiment analysis you need dictionaries of positive and negative words. The ones in this post were taken from GitHub. Below is the code for loading them into Python.

positive_words=open('XXXXXXXXXXXX').read().split('\n')
negative_words=open('XXXXXXXXXXXX').read().split('\n')

We now will make a function to calculate the sentiment

def sentiment_score(text,pos_list,neg_list):
   positive_score=0
   negative_score=0

   for w in text.split(' '):
      if w in pos_list:positive_score+=1
      if w in neg_list:negative_score+=1
   return positive_score-negative_score

Now we create an empty dictionary and run the analysis for Trump and then for Obama

tweets_sentiment={}
tweets_sentiment['trump']=[sentiment_score(tweet,positive_words,negative_words)for tweet in tweets['trump']]
tweets_sentiment['obama']=[sentiment_score(tweet,positive_words,negative_words)for tweet in tweets['obama']]

Now we can make visuals of our results with the code below

trump=plt.hist(tweets_sentiment['trump'],5)
obama=plt.hist(tweets_sentiment['obama'],5)

Obama is on the left and trump is on the right. It seems that trump tweets are consistently more positive. Below are the means for both.

numpy.mean(tweets_sentiment['trump'])
Out[133]: 0.36363636363636365

numpy.mean(tweets_sentiment['obama'])
Out[134]: 0.2222222222222222

Trump tweets are slightly more positive than Obama tweets. Below is the code for the Trump word cloud

1.png

Here is the code for the Obama word cloud

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A lot of speculating can be made from the word clouds and sentiment analysis. However, the results will change every single time because of the dynamic nature of Twitter. People are always posting tweets which changes the results.

Conclusion

This post provided an example of how to download and analyze tweets from twitter. It is important to develop a clear idea of what you want to know before attempting this sort of analysis as it is easy to become confused and not accomplish anything.

Word Clouds in Python

Word clouds are a type of data visualization in which various words from a dataset are actuated. Words that are larger in the word cloud are more common and words in the middle are also more common. In addition, some word clouds even use various colors to indicated importance.

This post will provide an example of how to make a word cloud using python. We will be using the “Women’s E-Commerce Clothing Reviews” available on the kaggle website.  We are going to only use the text reviews to make our word cloud even though other data is in the dataset. To prepare our dataset for making the word cloud we need to the following.

  1. Lowercase all words
  2. Remove punctuation
  3. Remove stopwords

After completing these steps we can make the word cloud. First, we need to load all of the necessary modules.

import pandas as pd
import re
from nltk.corpus import stopwords
import wordcloud
import matplotlib.pyplot as plt

We now need to load our dataset we will store it as the object ‘df’

df=pd.read_csv('YOUR LOCATION HERE')
df.head()

1.png

It’s hard to read but we will be working only with the “Review Text” column as this has the text data we need. Here is what our column looks like up close.

df['Review Text'].head()

Out[244]: 
0 Absolutely wonderful - silky and sexy and comf...
1 Love this dress! it's sooo pretty. i happene...
2 I had such high hopes for this dress and reall...
3 I love, love, love this jumpsuit. it's fun, fl...
4 This shirt is very flattering to all due to th...
Name: Review Text, dtype: object

We will now make all words lower case and remove punctuation with the code below.

df["Review Text"]=df['Review Text'].str.lower()
df["Review Text"]=df['Review Text'].str.replace(r'[-./?!,":;()\']',' ')

The first line in the code above lower cases all words. The second line removes the punctuation. The second line is trickier as you have to explain to python exactly what type of punctuation you want to remove and what to replace it with. Everything we want to remove is in the first set of single quotes. We want to replace the punctuation with a space which is the second set of single quotation marks with a space in the middle. THe r at the beginning of the parentheses stands for remove.

Here is what our data looks like after making these two changes

df['Review Text'].head()

Out[249]: 
0 absolutely wonderful silky and sexy and comf...
1 love this dress it s sooo pretty i happene...
2 i had such high hopes for this dress and reall...
3 i love love love this jumpsuit it s fun fl...
4 this shirt is very flattering to all due to th...
Name: Review Text, dtype: object

All the words are in lowercase. In addition, you can see that the dash in line 0 is gone as all the punctuation in the other lines. We now need to remove stopwords. Stopwords are the functional words that glue the meaning together without. Examples include and, for, but, etc. We are trying to make a cloud of substantial words and not stopwords so these words need to be removed.

If you have never done this on your computer before you may need to import the nltk module and run nltk.download_gui(). Once this is done you need to download the stopwords package.

Below is the code for removing the stopwords. First, we need to load the stopwords this is done below.

stopwords_list=stopwords.words('english')
stopwords_list=stopwords_list+['to']

We create an object called stopwords_list which has all the English stopwords. The second line just adds the word ‘to’ to the list. Nex,t we need to make an object that will look for the pattern of words we want to remove. Below is the code

pat = r'\b(?:{})\b'.format('|'.join(stopwords_list))

This code is the basically telling Python what to look for. Using regularized expressions Python will look for any word whos pattern on the left is the same as the pattern on the right after the .join function. Inside the .join function is our stopwords_list. We will now take this object called ‘pat’ and use it on our ‘Review Text’ variable.

df['Split Text'] = df['Review Text'].str.replace(pat, '')
df['Split Text'].head()df['Split Text'].head()

Out[258]: 
0      absolutely wonderful   silky  sexy  comfortable
1    love  dress     sooo pretty    happened  find ...
2       high hopes   dress  really wanted   work   ...
3     love  love  love  jumpsuit    fun  flirty   f...
4     shirt   flattering   due   adjustable front t...
Name: Split Text, dtype: object

You can see that we have created a new column called ‘Split Text’ and the results is a text that has lost many stop words.

We are now ready to make our word cloud below is the code and the output.

wordcloud1=wordcloud.WordCloud(width=1000,height=500).generate(' '.join(map(str, df['Split Text'])))
plt.figure(figsize=(15,8))
plt.imshow(wordcloud1)
plt.axis('off')

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This code is complex. We used the word cloud function and we had to use both generate map, and join as inner functions. All of these function were needed to take the words from the dataframe and make them simple text for the wordcloud function.

The rest of the code is common to mathplotlib so does not require much explanation. Ass you look at the word cloud, you can see that the most common words include top, look, dress, shirt, fabric. etc. This is reasonable given that these are women’s reviews of clothing.

Conclusion

This post provided an example of text analysis using word clouds in Python. The insights here are primarily descriptive in nature. This means that if the desire is prediction or classification other additional tools would need to build upon what is shown here.

KMeans Clustering in Python

Kmeans clustering is a technique in which the examples in a dataset our divided through segmentation. The segmentation has to do with complex statistical analysis in which examples within a group are more similar the examples outside of a group.

The application of this is that it provides the analysis with various groups that have similar characteristics which can be used to cater services to in various industries such as business or education. In this post, we will look at how to do this using Python. We will use the steps below to complete this process.

  1. Data preparation
  2. Determine the number of clusters
  3. Conduct analysis

Data Preparation

Our data for this examples comes from the sat.act dataset available in the pydataset module. Below is some initial code.

import pandas as pd
from pydataset import data
from sklearn.cluster import KMeans
from scipy.spatial.distance import cdist
import numpy as np
import matplotlib.pyplot as plt

We will now load our dataset and drop any NAs they may be present

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You can see there are six variables that will be used for the clustering. Next, we will turn to determining the number of clusters.

Determine the Number of Clusters

Before you can actually do a kmeans analysis you must specify the number of clusters. This can be tricky as there is no single way to determine this. For our purposes, we will use the elbow method.

The elbow method measures the within sum of error in each cluster. As the number of clusters increasings this error decrease. However,  a certain point the return on increasing clustering becomes minimal and this is known as the elbow. Below is the code to calculate this.

distortions = []
K = range(1,10)
for k in K:
kmeanModel = KMeans(n_clusters=k).fit(df)
distortions.append(sum(np.min(cdist(df, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / df.shape[0])

Here is what we did

  1. We made an empty list called ‘distortions’ we will save our results there.
  2. In the second line, we told R the range of clusters we want to consider. Simply, we want to consider anywhere from 1 to 10 clusters.
  3. Line 3 and 4, we use a for loop to calculate the number of clusters when fitting it to the df object.
  4. In Line 5, we save the sum of the cluster distance in the distortions list.

Below is a visual to determine the number of clusters

plt.plot(K, distortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Distortion')
plt.title('The Elbow Method showing the optimal k')

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The graph indicates that 3 clusters are sufficient for this dataset. We can now perform the actual kmeans clustering.

KMeans Analysis

The code for the kmeans analysis is as follows

km=KMeans(3,init='k-means++',random_state=3425)
km.fit(df.values)
  1. We use the KMeans function and tell Python the number of clusters, the type of, initialization, and we set the seed with the random_state argument. All this is saved in the objet called km
  2. The km object has the .fit function used on it with df.values

Next, we will predict with the predict function and look at the first few lines of the modified df with the .head() function.

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You can see we created a new variable called predict. This variable contains the kmeans algorithm prediction of which group each example belongs too. We then printed the first five values as an illustration. Below are the descriptive statistics for the three clusters that were produced for the variable in the dataset.

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It is clear that the clusters are mainly divided based on the performance on the various test used. In the last piece of code, gender is used. 1 represents male and 2 represents female.

We will now make a visual of the clusters using two dimensions. First, w e need to make a map of the clusters that is saved as a dictionary. Then we will create a new variable in which we take the numerical value of each cluster and convert it to a sting in our cluster map dictiojnary.

clust_map={0:'Weak',1:'Average',2:'Strong'}
df['perf']=df.predict.map(clust_map)

Next, we make a different dictionary to color the points in our graph.

d_color={'Weak':'y','Average':'r','Strong':'g'}

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Here is what is happening in the code above.

  1. We set the ax object to a value.
  2. A for loop is used to go through every example in clust_map.values so that they are colored according the color
  3. Lastly, a plot is called which lines upo the perf and clust values for color.

The groups are clearly separated when looking at them in two dimensions.

Conclusion

Kmeans is a form of unsupervised learning in which there is no dependent variable which you can use to assess the accuracy of the classification or the reduction of error in regression. As such, it can be difficult to know how well the algorithm did with the data. Despite this, kmeans is commonly used in situations in which people are trying to understand the data rather than predict.

Random Forest in Python

This post will provide a demonstration of the use of the random forest algorithm in python. Random forest is similar to decision trees except that instead of one tree a multitude of trees are grown to make predictions. The various trees all vote in terms of how to classify an example and majority vote is normally the winner. Through making many trees the accuracy of the model normally improves.

The steps are as follows for the use of random forest

  1. Data preparation
  2. Model development & evaluation
  3. Model comparison
  4. Determine variable importance

Data Preparation

We will use the cancer dataset from the pydataset module. We want to predict if someone is censored or dead in the status variable. The other variables will be used as predictors. Below is some code that contains all of the modules we will use.

import pandas as pd
import sklearn.ensemble as sk
from pydataset import data
from sklearn.model_selection import train_test_split
from sklearn import metrics
import matplotlib.pyplot as plt

We will now load our data cancer in an object called ‘df’. Then we will remove all NA’s use the .dropna() function. Below is the code.

df = data('cancer')
df=df.dropna()

We now need to make two datasets. One dataset, called X, will contain all of the predictor variables. Another dataset, called y, will contain the outcome variable. In the y dataset, we need to change the numerical values to a string. This will make interpretation easier as we will not need to lookup what the numbers represents. Below is the code.

X=df[['time','age',"sex","ph.ecog",'ph.karno','pat.karno','meal.cal','wt.loss']]
df['status']=df.status.replace(1,'censored')
df['status']=df.status.replace(2,'dead')
y=df['status']

Instead of 1 we now have the string “censored” and instead of 2 we now have the string “dead” in the status variable. The final step is to set up our train and test sets. We will do a 70/30 split. We will have a train set for the X and y dataset as well as a test set for the X and y datasets. This means we will have four datasets in all. Below is the code.

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

We are now ready to move to model development

Model Development and Evaluation

We now need to create our classifier and fit the data to it. This is done with the following code.

clf=sk.RandomForestClassifier(n_estimators=100)
clf=clf.fit(x_train,y_train)

The clf object has our random forest algorithm,. The number of estimators is set to 100. This is the number of trees that will be generated. In the second line of code, we use the .fit function and use the training datasets x and y.

We now will test our model and evaluate it. To do this we will use the .predict() with the test dataset Then we will make a confusion matrix followed by common metrics in classification. Below is the code and the output.

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You can see that our model is good at predicting who is dead but struggles with predicting who is censored. The metrics are reasonable for dead but terrible for censored.

We will now make a second model for the purpose of comparison

Model Comparision

We will now make a different model for the purpose of comparison. In this model, we will use out of bag samples to determine accuracy, set the minimum split size at 5 examples, and that each leaf has at least 2 examples. Below is the code and the output.

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There was some improvement in classify people who were censored as well as for those who were dead.

Variable Importance

We will now look at which variables were most important in classifying our examples. Below is the code

model_ranks=pd.Series(clf.feature_importances_,index=x_train.columns,name="Importance").sort_values(ascending=True,inplace=False)
ax=model_ranks.plot(kind='barh')

We create an object called model_ranks and we indicate the following.

  1. Classify the features by importance
  2. Set index to the columns in the training dataset of x
  3. Sort the features from most to least importance
  4. Make a barplot

Below is the output

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You can see that time is the strongest classifier. How long someone has cancer is the strongest predictor of whether they are censored or dead. Next is the number of calories per meal followed by weight and lost and age.

Conclusion

Here we learned how to use random forest in Python. This is another tool commonly used in the context of machine learning.

Decision Trees in Python

Decision trees are used in machine learning. They are easy to understand and are able to deal with data that is less than ideal. In addition, because of the pictorial nature of the results decision trees are easy for people to interpret. We are going to use the ‘cancer’ dataset to predict mortality based on several independent variables.

We will follow the steps below for our decision tree analysis

  1. Data preparation
  2. Model development
  3. Model evaluation

Data Preparation

We need to load the following modules in order to complete this analysis.

import pandas as pd
import statsmodels.api as sm
import sklearn
from pydataset import data
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn import tree
import matplotlib.pyplot as plt
from sklearn.externals.six import StringIO 
from IPython.display import Image 
from sklearn.tree import export_graphviz
import pydotplus

The ‘cancer’ dataset comes from the ‘pydataset’ module. You can learn more about the dataset by typing the following

data('cancer', show_doc=True)

This provides all you need to know about our dataset in terms of what each variable is measuring. We need to load our data as ‘df’. In addition, we need to remove rows with missing values and this is done below.

df = data('cancer')
len(df)
Out[58]: 228
df=df.dropna()
len(df)
Out[59]: 167

The initial number of rows in the data set was 228. After removing missing data it dropped to 167. We now need to setup up our lists with the independent variables and a second list with the dependent variable. While doing this, we need to recode our dependent variable “status” so that the numerical values are replaced with a string. This will help us to interpret our decision tree later. Below is the code

X=df[['time','age',"sex","ph.ecog",'ph.karno','pat.karno','meal.cal','wt.loss']]
df['status']=df.status.replace(1,'censored')
df['status']=df.status.replace(2,'dead')
y=df['status']

Next,  we need to make our train and test sets using the train_test_split function.  We want a 70/30 split. The code is below.

x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

We are now ready to develop our model.

Model Development

The code for the model is below

clf=tree.DecisionTreeClassifier(min_samples_split=10)
clf=clf.fit(x_train,y_train)

We first make an object called “clf” which calls the DecisionTreeClassifier. Inside the parentheses, we tell Python that we do not want any split in the tree to contain less than 10 examples. The second “clf” object uses the  .fit function and calls the training datasets.

We can also make a visual of our decision tree.

dot_data = StringIO()
export_graphviz(clf, out_file=dot_data, 
filled=True, rounded=True,feature_names=list(x_train.columns.values),
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) 
Image(graph.create_png())

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If we interpret the nodes furthest to the left we get the following

  • If a person has had cancer less than 171 days and
  • If the person is less than 74.5 years old then
  • The person is dead

If you look closely every node is classified as ‘dead’ this may indicate a problem with our model. The evaluation metrics are below.

Model Evaluation

We will use the .crosstab function and the metrics classification functions

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You can see that the metrics are not that great in general. This may be why everything was classified as ‘dead’. Another reason is that few people were classified as ‘censored’ in the dataset.

Conclusion

Decisions trees are another machine learning tool. Python allows you to develop trees rather quickly that can provide insights into how to take action.

Multiple Regression in Python

In this post, we will go through the process of setting up and a regression model with a training and testing set using Python. We will use the insurance dataset from kaggle. Our goal will be to predict charges. In this analysis, the following steps will be performed.

  1. Data preparation
  2. Model training
  3. model testing

Data Preparation

Below is a list of the modules we will need in order to complete the analysis

import matplotlib.pyplot as plt
import pandas as pd
from sklearn import linear_model,model_selection, feature_selection,preprocessing
import statsmodels.formula.api as sm
from statsmodels.tools.eval_measures import mse
from statsmodels.tools.tools import add_constant
from sklearn.metrics import mean_squared_error

After you download the dataset you need to load it and take a look at it. You will use the  .read_csv function from pandas to load the data and .head() function to look at the data. Below is the code and the output.

insure=pd.read_csv('YOUR LOCATION HERE')

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We need to create some dummy variables for sex, smoker, and region. We will address that in a moment, right now we will look at descriptive stats for our continuous variables. We will use the .describe() function for descriptive stats and the .corr() function to find the correlations.

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The descriptives are left for your own interpretation. As for the correlations, they are generally weak which is an indication that regression may be appropriate.

As mentioned earlier, we need to make dummy variables sex, smoker, and region in order to do the regression analysis. To complete this we need to do the following.

  1. Use the pd.get_dummies function from pandas to create the dummy
  2. Save the dummy variable in an object called ‘dummy’
  3. Use the pd.concat function to add our new dummy variable to our ‘insure’ dataset
  4. Repeat this three times

Below is the code for doing this

dummy=pd.get_dummies(insure['sex'])
insure=pd.concat([insure,dummy],axis=1)
dummy=pd.get_dummies(insure['smoker'])
insure=pd.concat([insure,dummy],axis=1)
dummy=pd.get_dummies(insure['region'])
insure=pd.concat([insure,dummy],axis=1)
insure.head()

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The .get_dummies function requires the name of the dataframe and in the brackets the name of the variable to convert. The .concat function requires the name of the two datasets to combine as well the axis on which to perform it.

We now need to remove the original text variables from the dataset. In addition, we need to remove the y variable “charges” because this is the dependent variable.

y = insure.charges
insure=insure.drop(['sex', 'smoker','region','charges'], axis=1)

We can now move to model development.

Model Training

Are train and test sets are model with the model_selection.trainin_test_split function. We will do an 80-20 split of the data. Below is the code.

X_train, X_test, y_train, y_test = model_selection.train_test_split(insure, y, test_size=0.2)

In this single line of code, we create a train and test set of our independent variables and our dependent variable.

We can not run our regression analysis. This requires the use of the .OLS function from statsmodels module. Below is the code.

answer=sm.OLS(y_train, add_constant(X_train)).fit()

In the code above inside the parentheses, we put the dependent variable(y_train) and the independent variables (X_train). However, we had to use the function add_constant to get the intercept for the output. All of this information is then used inside the .fit() function to fit a model.

To see the output you need to use the .summary() function as shown below.

answer.summary()

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The assumption is that you know regression but our reading this post to learn python. Therefore, we will not go into great detail about the results. The r-square is strong, however, the region and gender are not statistically significant.

We will now move to model testing

Model Testing

Our goal here is to take the model that we developed and see how it does on other data. First, we need to predict values with the model we made with the new data. This is shown in the code below

ypred=answer.predict(add_constant(X_test))

We use the .predict() function for this action and we use the X_test data as well. With this information, we will calculate the mean squared error. This metric is useful for comparing models. We only made one model so it is not that useful in this situation. Below is the code and results.

print(mse(ypred,y_test))
33678660.23480476

For our final trick, we will make a scatterplot with the predicted and actual values of the test set. In addition, we will calculate the correlation of the predict values and test set values. This is an alternative metric for assessing a model.

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You can see the first two lines are for making the plot. Lines 3-4 are for making the correlation matrix and involves the .concat() function. The correlation is high at 0.86 which indicates the model is good at accurately predicting the values. THis is confirmed with the scatterplot which is almost a straight line.

Conclusion

IN this post we learned how to do a regression analysis in Python. We prepared the data, developed a model, and tested a model with an evaluation of it.

Principal Component Analysis in Python

Principal component analysis is a form of dimension reduction commonly used in statistics. By dimension reduction, it is meant to reduce the number of variables without losing too much overall information. This has the practical application of speeding up computational times if you want to run other forms of analysis such as regression but with fewer variables.

Another application of principal component analysis is for data visualization. Sometimes, you may want to reduce many variables to two in order to see subgroups in the data.

Keep in mind that in either situation PCA works better when there are high correlations among the variables. The explanation is complex but has to do with the rotation of the data which helps to separate the overlapping variance.

Prepare the Data

We will be using the pneumon dataset from the pydataset module. We want to try and explain the variance with fewer variables than in the dataset. Below is some initial code.

import pandas as pd
from sklearn.decomposition import PCA
from pydataset import data
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt

Next, we will set up our dataframe. We will only take the first 200 examples from the dataset. If we take all (over 3000 examples), the visualization will be a giant blob of dotes that cannot be interpreted. We will also drop in missing values. Below is the code

df = data('pneumon')
df=df.dropna()
df=df.iloc[0:199,]

When doing a PCA, it is important to scale the data because PCA is sensitive to this. The result of the scaling process is an array. This is a problem because the PCA function needs a dataframe. This means we have to convert the array to a dataframe. When this happens you also have to rename the columns in the new dataframe. All this is done in the code below.

scaler = StandardScaler() #instance
df_scaled = scaler.fit_transform(df) #scaled the data
df_scaled= pd.DataFrame(df_scaled) #made the dataframe
df_scaled=df_scaled.rename(index=str, columns={0: "chldage", 1: "hospital",2:"mthage",3:"urban",4:"alcohol",5:"smoke",6:"region",7:"poverty",8:"bweight",9:"race",10:"education",11:"nsibs",12:"wmonth",13:"sfmonth",14:"agepn"}) # renamed columns

Analysis

We are now ready to do our analysis. We first use the PCA function to indicate how many components we want. For our first example, we will have two components. Next, you use the .fit_transform function to fit the model. Below is the code.

pca_2c=PCA(n_components=2)
X_pca_2c=pca_2c.fit_transform(df_scaled)

Now we can see the variance explained by component and the sum

pca_2c.explained_variance_ratio_
Out[199]: array([0.18201588, 0.12022734])

pca_2c.explained_variance_ratio_.sum()
Out[200]: 0.30224321247148167

In the first line of code, we can see that the first component explained 18% of the variance and the second explained 12%. This leads to a total of about 30%. Below is a visual of our 2 component model the color represents the race of the respondent. The three different colors represent three different races.

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Our two components do a reasonable separating the data. Below is the code for making four components. We can not graph four components since our graph can only handle two but you will see that as we increase the components we also increase the variance explained.

pca_4c=PCA(n_components=4)

X_pca_4c=pca_4c.fit_transform(df_scaled)

pca_4c.explained_variance_ratio_
Out[209]: array([0.18201588, 0.12022734, 0.09290502, 0.08945079])

pca_4c.explained_variance_ratio_.sum()
Out[210]: 0.4845990164486457

With four components we now have almost 50% of the variance explained.

Conclusion

PCA is for summarising and reducing the number of variables used in an analysis or for the purposes of data visualization. Once this process is complete you can use the results to do further analysis if you desire.

Data Exploration with Python

In this post, we will explore a dataset using Python. The dataset we will use is the Ghouls, Goblins, and Ghost (GGG) dataset available at the kaggle website. The analysis will not be anything complex we will simply do the following.

  • Data preparation
  • Data  visualization
  • Descriptive statistics
  • Regression analysis

Data Preparation

The GGG dataset is fictitious data on the characteristics of spirits. Below are the modules we will use for our analysis.

import pandas as pd
import statsmodels.regression.linear_model as sm
import numpy as np

Once you download the dataset to your computer you need to load it into Python using the pd.read.csv function. Below is the code.

df=pd.read_csv('FILE LOCATION HERE')

We store the data as “df” in the example above. Next, we will take a peek at the first few rows of data to see what we are working with.

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Using the print function and accessing the first five rows reveals. It appears the first five columns are continuous data and the last two columns are categorical. The ‘id’ variable is useless for our purposes so we will remove it with the code below.

df=df.drop(['id'],axis=1)

The code above uses the drop function to remove the variable ‘id’. This is all saved into the object ‘df’. In other words, we wrote over are original ‘df’.

Data Visualization

We will start with our categorical variables for the data visualization. Below is a table and a graph of the ‘color’ and ‘type’ variables.

First, we make an object called ‘spirits’ using the groupby function to organize the table by the ‘type’ variable.

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Below we make a graph of the data above using the .plot function. A professional wouldn’t make this plot but we are just practicing how to code.

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We now know how many ghosts, goblins and, ghouls there are in the dataset. We will now do a breakdown of ‘type’ by ‘color’ using the .crosstab function from pandas.

1.png

We will now make bar graphs of both of the categorical variables using the .plot function.

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We will now turn our attention to the continuous variables. We will simply make histograms and calculate the correlation between them. First the histograms

The code is simply subset the variable you want in the brackets and then type .plot.hist() to access the histogram function. It appears that all of our data is normally distributed. Now for the correlation

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Using the .corr() function has shown that there are now high correlations among the continuous variables. We will now do an analysis in which we combine the continuous and categorical variables through making boxplots

The code is redundant. We use the .boxplot() function and tell python the column which is continuous and the ‘by’ which is the categorical variable.

Descriptive Stats

We are simply going to calcualte the mean and standard deviation of the continuous variables.

df["bone_length"].mean()
Out[65]: 0.43415996604821117

np.std(df["bone_length"])
Out[66]: 0.13265391313941383

df["hair_length"].mean()
Out[67]: 0.5291143100058727

np.std(df["hair_length"])
Out[68]: 0.16967268504935665

df["has_soul"].mean()
Out[69]: 0.47139203219259107

np.std(df["has_soul"])
Out[70]: 0.17589180837106724

The mean is calcualted with the .mean(). Standard deviation is calculated using the .std() function from the numpy package.

Multiple Regression

Our final trick is we want to explain the variable “has_soul” using the other continuous variables that are available. Below is the code

X = df[["bone_length", "rotting_flesh","hair_length"]]

y = df["has_soul"]

model = sm.OLS(y, X).fit()

In the code above we crate to new list. X contains are independent variables and y contains the dependent variable. Then we create an object called model and use the  OLS() function. We place the y and X inside the parenthesis and we then use the .fit() function as well. Below is the summary of the analysis

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There is obviously a lot of information in the output. The r-square is 0.91 which is surprisingly high given that there were not high correlations in the matrix. The coefficiencies for the three independent variables are listed and all are significant. The AIC and BIC are for model comparison and do not mean much in isolation. The JB stat indicates that are distribution is not normal. Durbin watson test indicates negative autocorrelation which is important in time-series analysis.

Conclusion

Data exploration can be an insightful experience. Using Python, we found mant different patterns and ways to describe the data.

Logistic Regression in Python

This post will provide an example of a logistic regression analysis in Python. Logistic regression is commonly used when the dependent variable is categorical. Our goal will be to predict the gender of an example based on the other variables in the model. Below are the steps we will take to achieve this.

  1. Data preparation
  2. Model development
  3. Model testing
  4. Model evaluation

Data Preparation

The dataset we will use is the ‘Survey of Labour and Income Dynamics’ (SLID) dataset available in the pydataset module in Python. This dataset contains basic data on labor and income along with some demographic information. The initial code that we need is below.

import pandas as pd
import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from pydataset import data

The code above loads all the modules and other tools we will need in this example. We now can load our data. In addition to loading the data, we will also look at the count and the characteristics of the variables. Below is the code.

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At the top of this code, we create the ‘df’ object which contains our data from the “SLID”. Next, we used the .count() function to determine if there was any missing data and to see what variables were available. It appears that we have five variables and a lot of missing data as each variable has different amounts of data. Lastly, we used the .head() function to see what each variable contained. It appears that wages, education, and age are continuous variables well sex and language are categorical. The categorical variables will need to be converted to dummy variables as well.

The next thing we need to do is drop all the rows that are missing data since it is hard to create a model when data is missing. Below is the code and the output for this process.

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In the code above, we used the .dropna() function to remove missing data. Then we used the .count() function to see how many rows remained. You can see that all the variables have the same number of rows which is important for model analysis. We will now make our dummy variables for sex and language in the code below.

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Here is what we did,

  1. We used the .get_dummies function from pandas first on the sex variable. All this was stored in a new object called “dummy”
  2. We then combined the dummy and df datasets using the .concat() function. The axis =1 argument is for combing by column.
  3. We repeat steps 1 and 2 for the language variable
  4. Lastly, we used the .head() function to see the results

With this, we are ready to move to model development.

Model Development

The first thing we need to do is put all of the independent variables in one dataframe and the dependent variable in its own dataframe. Below is the code for this

X=df[['wages','education','age',"French","Other"]]
y=df['Male']

Notice that we did not use every variable that was available. For the language variables, we only used “French” and “Other”. This is because when you make dummy variables you only need k-1 dummies created. Since the language variable had three categories we only need two dummy variables. Therefore, we excluded “English” because when “French” and “Other” are coded 0 it means that “English” is the characteristic of the example.

In addition, we only took “male” as our dependent variable because if “male” is set to 0 it means that example is female. We now need to create our train and test dataset. The code is below.

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

We created four datasets

  • train dataset with the independent variables
  • train dataset with the dependent variable
  • test dataset with the independent variables
  • test dataset with the independent variable

The split is 70/30 with 70% being used for the training and 30% being used for testing. This is the purpose of the “test_size” argument. we used the train_test_split function to do this. We can now run our model and get the results. Below is the code.

1

Here is what we did

  1. We used the .Logit() function from statsmodel to create the logistic model. Notice we used only the training data.
  2. We then use the .fit() function to get the results and stored this in the result object.
  3. Lastly, we printed the results in the ‘result’ object using the .summary()

There are some problems with the results. The Pseudo R-square is infinity which is usually. Also, you may have some error output about hessian inversion in your output. For these reasons, we cannot trust the results but will continue for the sake of learning.

The coefficients are clear.  Only wage, education, and age are significant. In order to determine the probability you have to take the coefficient from the model and use the .exp() function from numpy. Below are the results.

np.exp(.08)
Out[107]: 1.0832870676749586

np.exp(-0.06)
Out[108]: 0.9417645335842487

np.exp(-.01)
Out[109]: 0.9900498337491681

For the first value, for every unit wages increaser the probability that they are male increase 8%. For every 1 unit increase in education there probability of the person being male decrease 6%. Lastly, for every one unit increase in age the probability of the person being male decrease by 1%. Notice that we subtract 1 from the outputs to find the actual probability.

We will now move to model testing

Model Testing

To do this we first test our model with the code below

y_pred=result.predict(X_test)

We made the result object earlier. Now we just use the .predict() function with the X_test data. Next, we need to flag examples that the model believes has a 60% chance or greater of being male. The code is below

y_pred_flag=y_pred>.6

This creates a boolean object with True and False as the output. Now we will make our confusion matrix as well as other metrics for classification.

1

The results speak for themselves. There are a lot of false positives if you look at the confusion matrix. In addition precision, recall, and f1 are all low. Hopefully, the coding should be clear the main point is to be sure to use the test set dependent dataset (y_test) with the flag data you made in the previous step.

We will not make the ROC curve. For a strong model, it should have a strong elbow shape while with a weak model it will be a diagonal straight line.

1.png

The first plot is of our data. The second plot is what a really bad model would look like. As you can see there is littte difference between the two. Again this is because of all the false positives we have in the model. The actual coding should be clear. fpr is the false positive rate, tpr is the true positive rate. The function is .roc_curve. Inside goes the predict vs actual test data.

Conclusion

This post provided a demonstration of the use of logistic regression in Python. It is necessary to follow the steps above but keep in mind that this was a demonstration and the results are dubious.

Working with a Dataframe in Python

In this post, we will learn to do some basic exploration of a dataframe in Python. Some of the task we will complete include the following…

  • Import data
  • Examine data
  • Work with strings
  • Calculating descriptive statistics

Import Data 

First, you need data, therefore, we will use the Titanic dataset, which is readily available on the internet. We will need to use the pd.read_csv() function from the pandas package. This means that we must also import pandas. Below is the code.

import pandas as pd
df=pd.read_csv('FILE LOCATION HERE')

In the code above we imported pandas as pd so we can use the functions within it. Next, we create an object called ‘df’. Inside this object, we used the pd.read_csv() function to read our file into the system. The location of the file needs to type in quotes inside the parentheses. Having completed this we can now examine the data.

Data Examination

Now we want to get an idea of the size of our dataset, any problems with missing. To determine the size we use the .shape function as shown below.

df.shape
Out[33]: (891, 12)

Results indicate that we have 891 rows and 12 columns/variables. You can view the whole dataset by typing the name of the dataframe “df” and pressing enter. If you do this you may notice there are a lot of NaN values in the “Cabin” variable. To determine exactly how many we can use is.null() in combination with the values_count. variables.

df['Cabin'].isnull().value_counts()
Out[36]: 
True     687
False    204
Name: Cabin, dtype: int64

The code starts with the name of the dataframe. In the brackets, you put the name of the variable. After that, you put the functions you are using. Keep in mind that the order of the functions matters. You can see we have over 200 missing examples. For categorical varable, you can also see how many examples are part of each category as shown below.

df['Embarked'].value_counts()
Out[39]: 
S    644
C    168
Q     77
Name: Embarked, dtype: int64

This time we used our ‘Embarked’ variable. However, we need to address missing values before we can continue. To deal with this we will use the .dropna() function on the dataset. THen we will check the size of the dataframe again with the “shape” function.

df=df.dropna(how='any')
df.shape
Out[40]: (183, 12)

You can see our dataframe is much smaller going 891 examples to 183. We can now move to other operations such as dealing with strings.

Working with Strings

What you do with strings really depends or your goals. We are going to look at extraction, subsetting, determining the length. Our first step will be to extract the last name of the first five people. We will do this with the code below.

df['Name'][0:5].str.extract('(\w+)')
Out[44]: 
1 Cumings
3 Futrelle
6 McCarthy
10 Sandstrom
11 Bonnell
Name: Name, dtype: object

As you can see we got the last names of the first five examples. We did this by using the following format…

dataframe name[‘Variable Name’].function.function(‘whole word’))

.str is a function for dealing with strings in dataframes. The .extract() function does what its name implies.

If you want, you can even determine how many letters each name is. We will do this with the .str and .len() function on the first five names in the dataframe.

df['Name'][0:5].str.len()
Out[64]: 
1 51
3 44
6 23
10 31
11 24
Name: Name, dtype: int64

Hopefully, the code is becoming easier to read and understand.

Aggregation

We can also calculate some descriptive statistics. We will do this for the “Fare” variable. The code is repetitive in that only the function changes so we will run all of them at once. Below we are calculating the mean, max, minimum, and standard deviation  for the price of a fare on the Titanic

df['Fare'].mean()
Out[77]: 78.68246885245901

df['Fare'].max()
Out[78]: 512.32920000000001

df['Fare'].min()
Out[79]: 0.0

df['Fare'].std()
Out[80]: 76.34784270040574

Conclusion

This post provided you with some ways in which you can maneuver around a dataframe in Python.

Numpy Arrays in Python

In this post, we are going to explore arrays is created by the numpy package in Python. Understanding how arrays are created and manipulated is useful when you need to perform complex coding and or analysis. In particular, we will address the following,

  1. Creating and exploring arrays
  2. Math with arrays
  3. Manipulating arrays

Creating and Exploring an Array

Creating an array is simple. You need to import the numpy package and then use the np.array function to create the array. Below is the code.

import numpy as np
example=np.array([[1,2,3,4,5],[6,7,8,9,10]])

Making an array requires the use of square brackets. If you want multiple dimensions or columns than you must use inner square brackets. In the example above I made an array with two dimensions and each dimension has it’s own set of brackets.

Also, notice that we imported numpy as np. This is a shorthand so that we do not have to type the word numpy but only np. In addition, we now created an array with ten data points spread in two dimensions.

There are several functions you can use to get an idea of the size of a data set. Below is a list with the function and explanation.

  • .ndim = number of dimensions
  • .shape =  Shares the number of rows and columns
  • .size = Counts the number of individual data points
  • .dtype.name = Tells you the data structure type

Below is code that uses all four of these functions with our array.

example.ndim
Out[78]: 2

example.shape
Out[79]: (2, 5)

example.size
Out[80]: 10

example.dtype.name
Out[81]: 'int64'

You can see we have 2 dimensions. The .shape function tells us we have 2 dimensions and 5 examples in each one. The .size function tells us we have 10 total examples (5 * 2). Lastly, the .dtype.name function tells us that this is an integer data type.

Math with Arrays

All mathematical operations can be performed on arrays. Below are examples of addition, subtraction, multiplication, and conditionals.

example=np.array([[1,2,3,4,5],[6,7,8,9,10]]) 
example+2
Out[83]: 
array([[ 3, 4, 5, 6, 7],
[ 8, 9, 10, 11, 12]])

example-2
Out[84]: 
array([[-1, 0, 1, 2, 3],
[ 4, 5, 6, 7, 8]])

example*2
Out[85]: 
array([[ 2, 4, 6, 8, 10],
[12, 14, 16, 18, 20]])

example<3
Out[86]: 
array([[ True, True, False, False, False],
[False, False, False, False, False]], dtype=bool)

Each number inside the example array was manipulated as indicated. For example, if we typed example + 2 all the values in the array increased by 2. Lastly, the example < 3 tells python to look inside the array and find all the values in the array that are less than 3.

Manipulating Arrays

There are also several ways you can manipulate or access data inside an array. For example, you can pull a particular element in an array by doing the following.

example[0,0]
Out[92]: 1

The information in the brackets tells python to access the first bracket and the first number in the bracket. Recall that python starts from 0. You can also access a range of values using the colon as shown below

example=np.array([[1,2,3,4,5],[6,7,8,9,10]]) 
example[:,2:4]
Out[96]: 
array([[3, 4],
[8, 9]])

In this example, the colon means take all values or dimension possible for finding numbers. This means to take columns 1 & 2. After the comma we have 2:4, this means take the 3rd and 4th value but not the 5th.

It is also possible to turn a multidimensional array into a single dimension with the .ravel() function and also to transpose with the transpose() function. Below is the code for each.

example=np.array([[1,2,3,4,5],[6,7,8,9,10]]) 
example.ravel()
Out[97]: array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])

example.transpose()
Out[98]: 
array([[ 1, 6],
[ 2, 7],
[ 3, 8],
[ 4, 9],
[ 5, 10]])

You can see the .ravel function made a one-dimensional array. The .transpose broke the array into several more dimensions with two numbers each.

Conclusion

We now have a basic understanding of how numpy array work using python. As mention before, this is valuable information to understand when trying to wrestling with different data science questions.

Lists in Python

Lists allow you to organize information. In the real world, we make list all the time to keep track of things. This same concept applies in Python when making list. A list is a sequence of stored data. By sequence, it is mean a data structure that allows multiple items to exist in a single storage unit. By making list we are explaining to the computer how to store the data in the computer’s memory.

In this post, we learn the following about list

  • How to make a list
  • Accessing items in a list
  • Looping through a list
  • Modifying a list

Making a List

Making a list is not difficult at all. To make one you first create a variable name followed by the equal sign and then place your content inside square brackets. Below is an example of two different lists.

numList=[1,2,3,4,5]
alphaList=['a','b','c','d','e']
print(numList,alphaList)
[1, 2, 3, 4, 5] ['a', 'b', 'c', 'd', 'e']

Above we made two lists, a numeric and a character list. We then printed both. In general, you want your list to have similar items such as all numbers or all characters. This makes it easier to recall what is in them then if you mixed them. However, Python can handle mixed list as well.

Access a List

To access individual items in a list is the same as for a sting. Just employ brackets with the index that you want. Below are some examples.

numList=[1,2,3,4,5]
alphaList=['a','b','c','d','e']

numList[0]
Out[255]: 1

numList[0:3]
Out[256]: [1, 2, 3]

alphaList[0]
Out[257]: 'a'

alphaList[0:3]
Out[258]: ['a', 'b', 'c']

numList[0] gives us the first value in the list. numList[0:3] gives us the first three values. This is repeated with the alphaList as well.

Looping through a List

A list can be looped through as well. Below is a simple example.

for item in numList :
    print(item)


for item in alphaList :
    print(item)


1
2
3
4
5
a
b
c
d
e

By making the two for loops above we are able to print all of the items inside each list.

Modifying List

There are several functions for modifying lists. Below are a few

The append() function as a new item to the list

numList.append(9)
print(numList)
alphaList.append('h')
print(alphaList)

[1, 2, 3, 4, 5, 9]
['a', 'b', 'c', 'd', 'e', 'h']

You can see our lists new have one new member each at the end.

You can also remove the last member of a list with the pop() function.

numList.pop()
print(numList)
alphaList.pop()
print(alphaList)
[1, 2, 3, 4, 5]
['a', 'b', 'c', 'd', 'e']

By using the pop() function we have returned our lists back to there original size.

Another trick is to merge lists together with the extend() function. For this, we will merge the same list with its self. This will cause the list to have duplicates of all of its original values.

numList.extend(numList)
print(numList)
alphaList.extend(alphaList)
print(alphaList)

[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]
['a', 'b', 'c', 'd', 'e', 'a', 'b', 'c', 'd', 'e']

All the values in each list have been duplicated. Finally, you can sort a list using the sort() function.

numList.sort()
print(numList)
alphaList.sort()
print(alphaList)
[1, 1, 2, 2, 3, 3, 4, 4, 5, 5]
['a', 'a', 'b', 'b', 'c', 'c', 'd', 'd', 'e', 'e']

Now all the numbers and letters are sorted.

Conclusion

THere is way more that could be done with lists. However, the purpose here was just to cover some of the basic ways that list can be used in Python.

Z-Scores and Inferential Stats in Python

In this post, we will look at some ways to calculate some inferential statistics in Python. We are mainly going to focus on z-scores and one/two-tailed test.

We will begin by import some needed packages and then we will make some data and plot it.  Below is the code and plot

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
quizscore=np.random.normal(69,9,60)
plt.hist(quizscore,density=True)

1

We create an object called “quizscore” which contains as you can tell quiz scores. In line four in the code above, we used the .random.normal function from the numpy package to create some random data. This function has 3 arguments in it, the first is the mean of the distribution, next is the standard deviation, and the last is the sample size. After this is the code for the histogram which uses the .hist function from the matplotlib.pyplot package.

Z-Scores

Z-Scores are a measure of how far a data point is from the mean. In the code below, we calculate the first five z-scores of our “quizscore” dataset.

stats.zscore(quizscore)[0:5,]
Out[127]: array([ 0.54362001, 1.56135871, -0.36924596, 0.53182556, -0.06014972])

In the output, you can see that the first number in the dataset is 0.54 standard deviations above the mean. Remember, our standard deviation was set to 9 so this means the score for the first quiz was about 73. We confirm this with the code below.

quizscore[0,]
Out[129]: 72.851341820695538

Fairly close, another question we can answer is what is the probability that someone got a score that was 1.5 standard deviations or higher on the quiz (about a score of 82). Below is the code followed by the answer.

1-stats.norm.cdf(1.5)
Out[132]: 0.066807201268858085

In the code above we subtract 1 from our code. The code uses the .cdf function. Inside the function, we put our z-score of 1.5. The answer is 0.066 or 6% of the scores have a Z-score of 1.5 or higher or a quiz score of around 82 or higher.

We can also determine what is the cutoff point for a certain high score. For us, we want to know what the cutoff for the top 15% of quiz scores. Below is the code.

stats.norm.ppf(0.85)
Out[136]: 1.0364333894937898

(1.03 * quizscore.std()) +quizscore.mean()
Out[137]: 77.748927759179054

In the code above, first we had to convert the percentage to a z-score and this is done with the .ppf function. The 85 percentile is equivalent to a Z-score of about 1. Next, we multiplied the z-score by the standard deviation of the quizscore and added the mean of the quizscore to this to get our final answer of 77.74. This means that a score of 77.74 and above is within the top 15%.

One & Two-Tailed Test

A one-tail test is used to compare the sample mean to a population mean. Another way to look at this is to convert a z-score to a p-value. What is happening here is you create a region in your distribution in which scores are considered unusual. For a one-tail test, this is the top 5%. For example, let’s say you want to know the probability that someone got a score of 92 or higher on the quiz. This can be solved with the following code.

quizZ=(92-quizscore.mean())/quizscore.std()
1-stats.norm.cdf(quizZ)
Out[138]: 0.0072370644085374414

Here is what we did

  1. We create an object called “quizZ” which took our specific score of 92 subtracted the mean of “quizscore” and divided it by the standard deviation of “quizscore”. This becomes the z-score for the value 92.
  2. We then subtract one from this while using the .cdf function
  3. The output indicates that there is less than a 1% chance that someone got a score of 92% or higher.

In this example our sample mean was small (only 1) but the concept remains the same.

A two-tailed test is the same concept except that the rejections regions are on both sides of the distribution and divide in half. This means that the top 2.5% and bottom 2.5% are now considered unusual scores. As an example, if the average score on the quiz is 75 with a standard deviation of 5 and we want to see if our class mean of 67 is different. We can use the following code to answer this.

two_tail=(quizscore.mean()-75)/5
stats.norm.cdf(two_tail)
Out[160]: 0.063688920253590395

You can see that the probability of our class is not unusual as it is above the cutoff of 5% indicating no difference. This means that if 75 is the center of our distribution the quiz score of 67 would be within 2 standard deviations of the mean.

Conclusion

In this post, we explored some of the ways you can do inferential statistics with Python. Whatever you want to know can be calculated by knowing a few lines of code.

Working with Strings in Python

It is somewhat difficult to define a string. In some ways, a string is text such as what is found in a book or even in this blog.  Strings are made up of characters such as letters and even symbols such as %#&@**. However, the computer does not use these characters but converts them to numbers for processing.

In this post, we will learn some of the basics of working with strings in Python. This post could go on forever in terms of what you can do with text so we will only address the following concepts

  • Finding individual characters
  • Modifying text

Finding Individual Characters

Finding individual characters in a string is simple. You simply need to type the variable name and after the name use brackets and put the number of the location of the character in the string. Below is an example

example="Educational Research Techniques"

print(example[0])
E

As you can see, we created a variable called “example” the content of “example” is the string “Educational Research Techniques”. To access the first letter we use the “print” function, type the name of the variable and inside brackets put the letter 0 for the first position in the string. This gives us the letter E. Remeber that Python starts from the number 0 and not 1.

If you want to start from the end of the string and go backward you simply use negative numbers instead of positive as in the example below.

example="Educational Research Techniques"
print(example[-1])
s

You can also get a range of characters.

example="Educational Research Techniques"
print(example[0:5])
Educa

The code above is telling python to take from position 0 to 4 but not including 5.

You can do even more creative things such as pick characters from different spots to create new words

example="Educational Research Techniques"
print(example[0:2] + example[20:25])
Ed Tech

Modifying Strings

It is also possible to modify the text in the string such as making all letters upper or lower case as shown below.

example.upper()
'EDUCATIONAL RESEARCH TECHNIQUES'

example.lower()
'educational research techniques'

The upper() function capitalizes and the lower() function make small case.

It is also possible to count the length of a string and swap the case with the len() and swapcase() functions respectively.

len(example)
31

example.swapcase()
'eDUCATIONAL rESEARCH tECHNIQUES'

Python allows you to count how many times a character appears using the count() function. You can also use the replace() to find characters and change them.

example.count('e')
4

example.replace('e','q')
'Educational Rqsqarch Tqchniquqs'

As you can see, the lower case letter ‘e’ appears four times. In the second line, python replaces all the lower case ‘e’s with the letter ‘q’.

Conclusion

Of course, there is so much more than we could do with strings. However, this post provides an introduction to what is possible.

while statements and Nested for loops in Python

When you are unsure how much data your application may need to process it is probably appropriate to use a while statements. The while statement we keep processing until it runs out of items to process. If you remember with a traditional for loop the limit is preset by the data structure you are analyzing.

Nested for loops is another concept we will look at. They are useful when it is unclear what the conditions of execution should be.

Since they can go on forever it is possible with a while statement to create an endless loop. This is a loop that never stops processing. This will essentially crash most computers and should naturally be avoided. To avoid this you need set the environment, state the while statement, and update the condition of the environment. Below is a simple example of this.

1

  1. In line 1, we have the environment for the condition which is a variable called “number” set to 0.
  2. Line 2 is the while statement which states that as long as “number” is less than 10 do the following.
  3. In line 3 the variable “number” is printed.
  4. In line 4, after “number” is printed the number 2 is added to the current value
  5. This takes place until the variable “number” is equal to ten.

Below is what the output looks like if you ran this

1

You can see that we start with 0. This is because we set the variable “number” originally to 0. Then the value increases by just as in line 4 of the code above.

Nested for Loops

Just as with functions you can also have nested for loops. This is a loop within a loop. Below is a simple example.

1.png

  1. Lines 1-2 as for input and you can type whatever you want
  2. Line 3-4 is the first for loop at it process whatever your input is from line 1. The final result is that the loop prints this
  3. Line 5-6 are the second for loop and simply processes whatever you inputted in line 2. This loop simply prints the input from line 2.

Here is what the output would look like

1.png

You can see that the loops took turns. The first loop ran its first letter them then the second loop ran everything. Then the first loop ran its second letter and the second loop ran everything again. Therefore, nested for loops affects the timing of when the code is ran.

Conclusion

This post looked at the use of while statements and for loops. while statements are useful when you do not know how long you may need to process data. for loops allow you to run complex looping in which you are trying to do multiple tasks.

for Loops in Python

The use of for loops are valuable when you need your application to do a repetitive task. Once the task is completed there is some sort of output that is returned. Understanding how to create a for loop is a critical step in utilizing the Python language.

Making for loops

Here is the basic syntax for a for loop

for item in data:
  do something

The word “for” indicates a for loop. The word “item” is an iteration variable. An iteration variable is a variable that changes value each time the loop goes through the data. It takes on the current value that is being analyzed for whatever purpose the loop has. You can name the iteration variable anything you want but a general rule is to use names that make sense for the context. Otherwise, nobody else will be able to understand your code.

After the colon is where you find “do something” here you put the command for whatever the loop is supposed to do. Below is an actual example of the use of the for loop.

1

Here is what happened

  1. At the top, we have our for loop. The iterator variable is “letter” and we are looping through the data of the string “education”.
  2. The next line is the action the for loop will perform. Essentially, the loop will pull each later from the string “education” and insert them one at a time into the phrase “Give me an”,. Notice how the word “letter” is at the end of our print statement. This the iteration variable that changes each time our for loop goes through the string “education.
  3. The output is several print statements each containing a different letter from the string “education”

for loops with Breaks

Breaks are used to provide conditions in which the loop will stop. In the example below, we add some code to our cheer that allows you to enter your own cheer. However, the church must be less than 10 letters otherwise you get a message that word is too long. Below is the code

1

Here is what it does.

  1. In line 1, you provide a word as indicated by the instructions in the parentheses.
  2. Line 2 is the for loop. letter is the iteration variable for our word in “Value”
  3. Line 3 is the if statement. The strong on “Value” is checked to make sure it is 10 characters or less.
  4. In line 4, if “Value” is greater than 10 characters you get the message that the cheer is too long.
  5. Line 5 is the break which stops the loop from continuing.
  6. In line 6, if the word is less than 10 characters you get the cheer with each letter.

Below is the output with less than 10 characters

1

Here is the output with more than 10 characters

1

Continue and for loop

The continue clause allows you to check the data and only process it based on certain conditions. In the code below, we are going to change our cheer code so that it removes spaces when making the cheer.

1

The code is mostly the same with a few exceptions

  • The if statement looks for blank spaces and these are left out of the cheer.
  • The continue clause tells python to keep going
  • Finally, the cheer is given

Below is what the output looks like if you ran this code

1

You can see that I put many blank spaces in-between the letters but these do not appear in the code. This is because of the continue clause.

Conclusion

for loops are a basic yet powerful tool of programming. In Python, for loops are used for the same reason as other languages and that is for completing repetitive tasks. The examples, here provide some simple ways in which this can be done.

elif Clause and Nested Statements in Python

This post will provide a brief introduction into the use of the elif clause and nested statements in Python.

elif Clause

The elif clause is used to add an additional set of conditions to an if statement. If you have ever used some sort of menu on a computer in which you had to make several choices it is possible that an elif clause was involved in the code.

The syntax for the elif clause is the same as for the if statement. Below is a simple example that employs the elif clause.

1.png

Here is what this code does.

  1. In lines 1-3, I print three lines of code at the beginning. These are the choices available to the user.
  2. In line 4, the “pick” variable stores whatever number the user inputs through the “input” function. It must be an integer which is why I used the “int” function
  3. In line 5 we begin the if statement. If the “pick” variable is set to 1 you can see the printout in line 6.
  4. In lines 7 and 8 we use the elif clause. The settings are mostly the same as in the if statement in line 5 except the “pick” variable is set to different numbers with different outputs.
  5. Lastly, in line 11 we have the else clause. If for any reason the person picks something besides 1,2 or 3 this message will appear.

What this code means in simple English is this

  • If the pick is 1 then print “dogs are your favorite animal”
  • or else if the pick is 2 then print “cats are your favorite animal”
  • or else if the pick is 3 then print “rabbits are your favorite animal”
  • else print “I do not understand your choice”

Here is what the code looks like when you run it

1

As a note, if you type in a letter or string you will get an error message. This is because our code is not sophisticated enough to deal with non-integers at this point.

Nested Statements

Just like with functions which can be nested so can decision statements by nesting inside each other. To put this simply the conditions set by the first if statement can potentially affect the second condition. This is explained better with an example.

1.png

Here is what is happening in the code above.

  1. In lines 1 and 2 the user has to pick numbers that are saved in the variables “num1” and “num2.”
  2. Line 3 and 4 are the if statements. Line 3 and line 9 are the outer if statement and line 4-8 are the inner if statement.
  3. Line 3 shares that “num1” must be between 1 and 10.
  4. Line 4 shares that “num2” must be between 1 and 10.
  5. Line 5 is the results of the inner if statement. The results are printed using the “.format” method where the {0} and {1} are the variables “num1” and “num2”. After the comma is what is done to the variables, they are simply added together.
  6. Line 8 is the else clause. If someone types something different form a number between 1-10 for the second number they will see this message
  7. Line 9 is the else clause for the outer if statement. This is only seen if a value different from 1-10 is inputted.

If you run the code here is what it should look like

1.png

Conclusion

The elif clause and nested decision statements are additional tools that can be used to empower your applications. This is some of the most basic ideas in using a language such as Python.

Logical Flow in Python

Applications often have to make decisions and to do this they need a set of conditions to him them decide what to do. Python, like all programming languages, has tools that allow the application to execute various actions based on conditions. In this post, we will look at the use of if statements.

If Statement Define

An if statement is a statement used in Python that determines when an action should happen. At a minimum, an if statement should have two parts to it. The first part is the condition and the second part is the action that Python performs if the condition is true. Below is what this looks like

if SOMETHING IS TRUE:
    DO THIS

This is not the most beautiful code but this is the minimum for a if statement in Python.

If Statement 

Below is the actual application of the use of an if statement

 number=5
if number ==5:
   print("Correct")
 
Correct

In the code above we created a variable called number and set its value to 5. Then we created the if statement by setting the condition as “if number equals 5” the action for this being true was prin the string “correct”, which Python does at the bottom.

Notice the double equal sign used in the if statement. The double equal sign is used for relational equality while the single equal sign is used for assigning values to variables. Also, notice the colon at the end of the if statement. This must be there in order to complete the code.

There is no limit to the number of tasks an if statement can perform. In to code above the, if statement only printed “Correct” however, we can put many more print functions or other actions as shown below.

number=5
if number ==5:
   print("Correct")
   print("Done")
 
Correct
Done

All that is new above is that we have two print statements that were executed because the if statement was true.

Multiple Comparisons

In the code above python only had to worry about whether number equaled 5. However, we can have Python make multiple comparisons as well below is an example.

number=5
if (number>0) and (number<10):
 print("Correct")
 print("Finished")
 
Correct
Finished

Now for the print functions to execute the number has to be greater than 0 but less than 10. If you change the number to something less than 0 or greater than 10 nothing will happen when you run the code because the conditions were not met. In addition, if you type in a letter like ‘a’ you will get an error message because Python is anticipating a number and not a string.

If Else Statements

The use of an else clause in an if statement allows for an alternative task to be executed if the first conditions in the if statement are not met. This allows the application to do something when the conditions are not met rather than leaving a blank screen. Below we modify our code in the previous example slightly so that something happens because of the else clause in the if statement.

number=15
if (number>0) and (number<10):
 print("Correct")
 print("Finished")
else:
 print("Number is out of range")
 
Number is out of range

In this code above we did the following

  1. we set our number to 15
  2. We created an if statement that searches for a number greater than 0 and less than 10.
  3. If the conditions in step 2 are true we print the two statements
  4. If the conditions in step 2 are not met we print the statement after the else clause
  5. Our number was 15 so Python printed the statement after the else clause

It can get much more complicated and powerful than this but I think this is clear and enough for now.

Conclusion

This post provided an introduction to the if statement in Python. THe if statement will execute a command based on one or more conditions. You must be careful with the syntax. Lastly, you can also include an alternative task with the else clause if the conditions are not met.

Making Functions in Python

Efficiency is important when writing code. Why would you type something a second time if you do not have to? One way coders save time, reduce error, and make their code easier to read is through the use of functions.

Functions are a way of storing code so that it can be reused without having to retype or code it again. By saving a piece of reusable code as a function you only have to call the function in order to use it again. This improves efficiency and reliability of your code. Functions simply take an input, rearrange the input however it is supposed to, and provide an output. In this post, we will look at how to make a function using Python.

Simple Function

A function requires the minimum information

  • A name
  • Determines if any requirements (arguments) are needed
  • The actual action the function is supposed to do to the input

We will now make a simple function as shown in the code below.

def example():
     print("My first function")

In the code above we have the three pieces of information.

  • The name of the function is “example” and you set the name using the “def” command.
  • There are no requirements or arguments in this function. This is why the parentheses are empty. This will make more sense later.
  • What this function does is use the “print” function to print the string “My first function”

We will now use the function by calling it in the example below.

example()
My first function

As you can see, when we call the function it simply prints the string. This function is not that impressive but it shows you how functions work.

Functions with Arguments

Arguments are found with the parentheses of a function. They are placeholders for information that you must supply in order for the function to work. Below is an example.

def example(info):
    print(info)

Now our “example” function has a required argument called “info” we must always put something in place of this for the function to run. Below is an example of us calling the “example” function with a string in place of the argument  “info”.

example("My second function")
My second function

You can see that the function simply printed what we placed in the paratheses. If we had left the parentheses empty we would have gotten an error message. You can try that yourself.

You can assign a default value to your argument. This is useful if people do not provide their own value. Below we create the same function but with a default value for the argument.

def example(info="You forgot to give a value"):
    print(info)

We will now call it but we will not include the argument

example()
You forgot to give a value

return and print

When creating functions, it is common to have to decide when to use the “return” or “print” function. Below are some guidelines

  • Print is for people. If a person only needs to see the output without any other execution the print is a good choice.
  • Return is appropriate when sending the data back to the caller for additional execution. For example, using one function before using a second function

If you take any of the examples and use “return” instead of “print” they will still work so the difference between “return” and “print” depends on the ultimate purpose of the application.

Conclusion

Functions play a critical role in making useful applications. This is due to their ability to save time for coders. THere are several concepts to keep in mind when developing functions. Understanding these ideas is important for future success.

Common Data Types in Python

All programming languages have a way of storing certain types of information in variables. Certain data types or needed for one situation and different data types for another. It is important to know the differences in the data types otherwise serious problems could arise when developing an application. In this post, we will look at some of the more commonly used data types in Python.

Making Variables

It is first important to understand how to make a variable in Python. It is not that complicated. The format is the following

variable name =  data inside the variable

You simply type a name, use the equal sign, and then include the data to be saved in the variable. Below is an example where I save the number 3 inside a variable called “example”

example=3
print(example)
3

The “print” function was used to display the contents of the “example” variable.

Numeric Types

There are two commonly used numeric data types in Python and they are integers and floating point values.

Integers

Integers are simply whole positive or negative numbers. To specifically save a number as an integer you place the number inside the “int” before saving as a variable as in the example below.

example=int(3)

print(example)
3

You can check the data type by using the “type” function on your variable. This is shown below.

type(example)
Out[17]: int

The results are “int” which stands for integer.

Floating-Point Types

Floating-point numbers are numbers with decimals. If your number includes a decimal it will automatically be stored as a floating type. If your number is a whole number and you want to save it as a floating type you need to use the “float” function when storing the data. Below are examples of both

#This is an example of a float number

example=3.23

print(example)
3.23

#This is an example of converting a whole number to a floating point

example=float(3)

print(example)
3.0

Floating points can store exponent numbers using scientific notation. Floating point numbers are used because decimals are part of the real world. The downside is they use a lot of memory compared to integers.

Other Types

We will look at two additional data types and they are boolean and string.

Boolean

A boolean variable only has two possible values which are True or False. This seems useless but it is powerful when it is time to have your application do things based on conditions. You are not really limited to True or False you can also type in mathematical expressions that Python evaluates. Below are some examples.

#Variable set to True

example=True

print(example)
True

#Variable set to True after evaluting an expression

example=1<2

print(example)
True

String

A string is a variable that contains text. The text is always enclosed in quotations and can be numbers, text, or a combination of both.

example="ERT is an awesome blog"

print(example)
ERT is an awesome blog

Conclusion

Programming is essentially about rearranging data for various purposes. Therefore, it only makes sense that there would be different ways to store data. This post provides some common forms in which data can manifest itself while using Python.

Intro to Python

Python is a highly popular programming language. It is so popular that it is now the most commonly used programming language for machine learning/data science purposes having surpassed R.

However, Python is not limited to just statistical tools. Python is also used by many companies for a host of reasons including Yahoo, Dropbox,  Google, NASA, IBM, and Mozilla.

One secret to Pythons popular is its flexibility. When using Python it is possible to employ several different coding styles. Below is just some of them.

  • Procedural: This is the simplest form of coding and involves executing each line of code sequential.
  • Functional: Functions are used to transform data as found in mathematics.
  • Imperative: Employs statements to achieve a goal
  • Object-oriented: The use of objects (aka data structures) to model the real world. Not fully implemented in Python.

You can mix these styles together to make powerful applications.

Using Python

You can download Python by searching for “Anaconda Python” in Google. The Anaconda version of Python downloads several additional features to besides Python including the Spyder IDE which is what we will use here.

Once you download and install Anaconda, on your computer you need to search for the program called “SPyder”. When you open it you will see the following.

1.png

Here is what each pane represents.

  • To the left is the text editor, you can type code that you want to save here.
  • In the top right is the variable explorer. Here you can find a list of the objects you have made.
  • In the bottom right is the Interactive Python console or “IPython” console for short. Here you can type code quickly without the need of storing it for long-term use. In addition, the results of any code execution is normally displayed here as well

When writing code remember that you can save it long term in the text editor or just execute it quickly in  the console,

First Line of Code

We will now run our first line of code. Followed by the output in the IPython Console. Below is what we typed into the Console

print("Hello to Python")

Here is what it looks like in the console

1

Here is what we did.

  1. We typed “print(“Hello to Python”)” into the console. This is an example of the use of a function.
  2. The output provides several pieces of information.
    • The blue shows what line this is in the console. In other words, this is the third line of code I had typed in the console. You may have a different number.
    • The purple is the function being used which for us is the “print” function which simply displays the input
    • The green is the argument that the function is changing. Our argument is a string of text that is put in quotes deliberately
    • The text in black is the actual output

Of course, there is much more to Python then this. However, this serves as an introduction for a future post.

Conclusion

Python is a popular programming language used in a variety of application. The source of its popularity has to do with it general-purpose philosophy. There’s a little bit of something for everybody in this language which encourages its use. Using the Spyder IDE will allow you to experience Python for the purpose of acquiring new skills.