Cheat Sheet: Data Wrangling

 


Package/MethodDescriptionCode Example
Replace missing data with frequencyReplace the missing values of the data set attribute with the mode common occurring entry in the column.
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  1. MostFrequentEntry = df['attribute_name'].value_counts().idxmax() 
  2. df['attribute_name'].replace(np.nan,MostFrequentEntry,>df['attribute_name'].replace(np.nan,MostFrequentEntry, inplace=True)
Replace missing data with meanReplace the missing values of the data set attribute with the mean of all the entries in the column.
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  1. AverageValue=df['attribute_name'].astype(<data_type>).mean(axis=0)
  2. df['attribute_name'].replace(np.nan, AverageValue, inplace=True)
Fix the data typesFix the data types of the columns in the dataframe.
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  1. df[['attribute1_name', 'attribute2_name', ...]] = 
  2. df[['attribute1_name', 'attribute2_name', ...]].astype('data_type')
  3. #data_type is int, float, char, etc.
Data NormalizationNormalize the data in a column such that the values are restricted between 0 and 1.
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  1. df['attribute_name'] = 
    df['attribute_name']/df['attribute_name'].max()
BinningCreate bins of data for better analysis and visualization.
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  1. bins = np.linspace(min(df['attribute_name']), 
  2. max(df['attribute_name'],n)
  3. # n is the number of bins needed 
  4. GroupNames = ['Group1','Group2','Group3,...]
  5. df['binned_attribute_name'] = 
  6. pd.cut(df['attribute_name'], bins, labels=GroupNames, include_lowest=True)
Change column nameChange the label name of a dataframe column.
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  1. df.rename(columns={'old_name':\'new_name'}, inplace=True)
Indicator VariablesCreate indicator variables for categorical data.
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  1. dummy_variable = pd.get_dummies(df['attribute_name'])
  2. df = pd.concat([df, dummy_variable],axis = 1)

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