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)