Pandas is a powerful data analysis and manipulation library for Python. This cheat sheet is a quick reference for Pandas beginners.
You’ll need to import pandas to get started:
import pandas as pd
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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 |
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df.head() |
First 5 rows |
df.tail() |
Last 5 rows |
df.shape |
Number of rows and columns |
df.info() |
Info on DataFrame |
df.describe() |
Summary statistics |
df.columns |
Column names |
df.index |
Index |
df.dtypes |
Data types of columns |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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'].dt.day |
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 |