A very quick and simple reference for the Python Pandas Module.
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Import Pandas Module with the alias pd
import pandas as pd |
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From a csv file
df = pd.read_csv('file.csv') |
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From a python dictionary
df = pd.DataFrame.from_dict(<dict>) |
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Display first five rows in dataframe
df.head() |
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Display last five rows in dataframe
df.tail() |
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Show all column names
df.columns |
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Show all object types in dataframe
df.dtypes |
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Show statistics for all int and float columns
df.describe() |
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Show statistics for 'object' type columns
df.describe(include='object') |
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Show number of rows and columns
df.shape |
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Display True for each NaN value, False otherwise
df.isnull() |
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Display a table with the number of NaN values for each column
df.isnull().sum() |
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Delete all rows containing NaN values in the df Dataframe
df.dropna(inplace=True) |
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Delete 'col_name' column
df.drop('col_name', axis=1) |
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Example of a calculated column
df['new_col'] = df['col_1'] + df['col_2'] |
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Update the entire column to value <value>
df['new_col'] = <value> |
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Update the cell at (a,b) to <value>
df.iloc[a,b] = <value> |
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Update (or creates) 'col_a' with the result of lambda function applied to 'col_b'
df['col_a'] = df['col_b'].apply(<lambda function>) |
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Display an entire column as a series
df['column_name'] |
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Display all columns in the given list
df[['col_1', 'col_2', ... 'col_n' ]] |
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Show all unique elements in 'column_name'
df['column_name'].unique() |
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Display all rows satisfying <condition>
df[<condition>] |
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Display all rows where
df['col_name'] == <value>df[df['col_name'] == <value>] |
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Show all rows satisfying both conditions
df[(<condition_1>]) & (<condition_2>)] |
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Displays the entire row indexed n
df.iloc[n] |
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Displays the element in row n & column m
df.iloc[n, m] |
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Displays a slice of rows: from row a to row b
df.iloc[a:b] |
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Displays rows a to b only in the columns c to d
df[a:b, c:d] |
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Shows all rows indexed with
<ind>df.loc[<ind>] |
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Create a copy of the dataframe
new_df = df.copy() |
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Set 'column_name' as the index
df.set_index('column_name', inplace=True) |
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Output to csv file
df.to_csv('output.csv') |
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Output to json file
df.to_json() |
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Output to html file
df.to_html() |
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Delete a Dataframe
del df |
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