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Tuesday, December 10, 2019

Exploratory Data Analysis EDA Cheat Sheet

Pandas


  • pandas.DataFrame.shape -- > (row_count, col_count)
  • pandas.DataFrame.shape[0] --> number of records, number of samples in the dataset
  • my_dataframe['my_series_name'].unique() --> returns a unique values of a column, "radio button choices"
  • dataframe.describe() --> returns summary data
  • len(my_dataframe['my_series_name'].unique()) --> number of unique values
  • import os os.listdir('name_of_directory_or_just_use_.') --> list the files in the current directory '.' os.listdir('.')  or a specific directory with a name 
  • import os len(os.listdir('.') ) --> returns the number of files in the current directory
  • my_dataframe.groupby(['col_1', 'col_2']) --> groupby column 1 first then groupby column 2
  • Converting a Pandas GroupBy output from Series to DataFrame: .groupby() returns a groupby object with MultiIndex instead of a dataframe with a single index. it is also known as a hierarchical index. Will need to rename columns and reset index my_groupby.add_suffix('_Count').reset_index() or call the .size().reset_index() important to note that .size() is called on the groupby object not the usual dataframe. pandas.core.groupby.GroupBy.size calculates : Series Number of rows in each group
  • group = ['col_1', 'col_2']; my_df.groupby(group).size().reset_index(name="colum_name")
  • df = df[(df.col_name < 1) & (df.col_name_2 < 1)] complex condition query / filter in dataframe
  • pd = pd.query('col_name != "my_value"')
  • .value_count df.column.value_count()
  • pandas cheatsheet
  • .copy()
  • .head()
  • .unique()
  • Another way to use unique pd.unique(df.col_name)
  • df['col_name'].isnull().sum()
  • df['col_name'].min() 
  • df['col_name'].max()
  • df.fillna(0) #fill the dataframe with zero the entire table
  • df.reset_index(drop=True, inplace = True)
  • remove target column or any column data.drop(['target'], axis = 1, inplace = True)


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