Pandas is a powerful data analysis and manipulation library for Python. This cheat sheet is a quick reference for Pandas beginners.

#Getting Started


You’ll need to import pandas to get started:

import pandas as pd

#Creating DataFrames

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pd.DataFrame(data={'col1': [1, 2], 'col2': [3, 4]}) From a dictionary
pd.DataFrame(data=[{'a': 1, 'b': 2}, {'a': 3, 'b': 4}]) From a list of dictionaries
pd.read_csv('file.csv') From a CSV file
pd.read_excel('file.xlsx') From an Excel file

#Inspecting Data

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df.head() First 5 rows
df.tail() Last 5 rows
df.shape Number of rows and columns Info on DataFrame
df.describe() Summary statistics
df.columns Column names
df.index Index
df.dtypes Data types of columns

#Selecting Data

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df['col1'] Select column
df[['col1', 'col2']] Select multiple columns
df.loc[0] Select row by index
df.loc[:, 'col1'] Select all rows for 'col1'
df.iloc[0] Select row by position
df.iloc[0, 1] Select specific value
df[df['col1'] > 2] Select rows based on condition

#Data Cleaning

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df.dropna() Drop rows with any missing values
df.dropna(axis=1) Drop columns with any missing values
df.fillna(0) Replace missing values with 0
df.drop_duplicates() Drop duplicate rows
df.rename(columns={'old_name': 'new_name'}) Rename columns
df.astype('int') Change data type

#Adding/Removing Data

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df['col3'] = df['col1'] + df['col2'] Add new column
df.drop('col1', axis=1) Drop column
df.append(new_row) Add new row
df.insert(2, 'new_col', new_data) Insert new column at position 2

#Combining Data

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pd.concat([df1, df2]) Concatenate rows
pd.concat([df1, df2], axis=1) Concatenate columns
pd.merge(df1, df2, on='key') Merge DataFrames on key
pd.merge(df1, df2, left_on='key1', right_on='key2') Merge on different keys
df1.join(df2, lsuffix='_left', rsuffix='_right') Join DataFrames

#Aggregating Data

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df['col1'].sum() Sum of values in column
df['col1'].mean() Mean of values in column
df['col1'].count() Count of values in column
df['col1'].min() Minimum value in column
df['col1'].max() Maximum value in column
df['col1'].std() Standard deviation
df['col1'].var() Variance
df.groupby('col1').sum() Group by and sum
df.groupby('col1').mean() Group by and mean
df.groupby(['col1', 'col2']).count() Group by multiple columns

#Applying Functions

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df.apply(np.sqrt) Apply function to all values
df['col1'].apply(lambda x: x ** 2) Apply function to column
df.applymap(str) Apply function to DataFrame elements
df['col1'].map({'a': 1, 'b': 2}) Map values
df['col1'].replace('a', 1) Replace values

#Handling Dates

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df['date'] = pd.to_datetime(df['date']) Convert to datetime
df['year'] = df['date'].dt.year Extract year
df['month'] = df['date'].dt.month Extract month
df['day'] = df['date'] Extract day
df.set_index('date', inplace=True) Set date as index


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df.to_csv('file.csv') Save DataFrame to CSV
df = pd.read_csv('file.csv') Load DataFrame from CSV
df.to_excel('file.xlsx') Save DataFrame to Excel
df = pd.read_excel('file.xlsx') Load DataFrame from Excel
from sqlalchemy import create_engine Import SQLAlchemy for SQL operations
engine = create_engine('sqlite:///:memory:') Create SQL engine
df.to_sql('table_name', engine) Save to SQL table
df = pd.read_sql('table_name', engine) Load from SQL table