Cheat Sheet: Exploratory Data Analysis

 

Cheat Sheet: Exploratory Data Analysis

Package/MethodDescriptionCode Example
Complete dataframe correlationCorrelation matrix created using all the attributes of the dataset.
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  1. df.corr()
Specific Attribute correlationCorrelation matrix created using specific attributes of the dataset.
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  1. df[['attribute1','attribute2',...]].corr()
Scatter PlotCreate a scatter plot using the data points of the dependent variable along the x-axis and the independent variable along the y-axis.
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  1. from matlplotlib import pyplot as 
  2. plt plt.scatter(df[['attribute_1']],df[['attribute_2']])
Regression PlotUses the dependent and independent variables in a Pandas data frame to create a scatter plot with a generated linear regression line for the data.
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  1. import seaborn as sns 
  2. sns.regplot(x='attribute_1',y='attribute_2', data=df)
Box plotCreate a box-and-whisker plot that uses the pandas dataframe, the dependent, and the independent variables.
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  1. import seaborn as sns 
  2. sns.boxplot(x='attribute_1',y='attribute_2', data=df)
Grouping by attributesCreate a group of different attributes of a dataset to create a subset of the data.
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  1. df_group = df[['attribute_1','attribute_2',...]]
GroupBy statementsa. Group the data by different categories of an attribute, displaying the average value of numerical attributes with the same category.
b. Group the data by different categories of multiple attributes, displaying the average value of numerical attributes with the same category.
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  1. a.
  2. df_group = 
  3. df_group.groupby(['attribute_1'],as_index=False).mean() 
  4. b. 
  5. df_group = df_group.groupby(['attribute_1', 
  6. 'attribute_2'],as_index=False).mean()
Pivot TablesCreate Pivot tables for better representation of data based on parameters
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  1. grouped_pivot = 
  2. df_group.pivot(index='attribute_1',columns='attribute_2')
Pseudocolor plotCreate a heatmap image using a PsuedoColor plot (or pcolor) using the pivot table as data.
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  1. from matlplotlib import pyplot as plt 
  2. plt.pcolor(grouped_pivot, cmap='RdBu')
Pearson Coefficient and p-valueCalculate the Pearson Coefficient and p-value of a pair of attributes
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  1. From scipy import stats 
  2. pearson_coef,p_value=stats.pearsonr(df['attribute_1'], 
  3. df['attribute_2'])

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