📊 数据科学
Pandas
Pandas Cheat Sheet - 快速参考指南，收录常用语法、命令与实践。
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#pandas#python
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Getting Started
Introduction
You’ll need to import pandas to get started:
PYTHON
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import pandas as pd
Creating DataFrames
| - | - |
|---|---|
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
| - | - |
|---|---|
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 |
Selecting Data
| - | - |
|---|---|
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
| - | - |
|---|---|
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
| - | - |
|---|---|
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
| - | - |
|---|---|
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
| - | - |
|---|---|
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
| - | - |
|---|---|
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
| - | - |
|---|---|
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 |
Input/Output
| - | - |
|---|---|
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 |
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