Pandas Cheat Sheet
Python Pandas library reference guide
Pandas Cheat Sheet
Quick reference guide for Pandas DataFrame operations. Click the copy button to copy any command.
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Total Commands
93
Filtered Results
Importing
| Command | Copy | Description |
|---|---|---|
import pandas as pd | Import pandas library | |
import numpy as np | Import numpy (often used with pandas) |
Reading Data
| Command | Copy | Description |
|---|---|---|
pd.read_csv("file.csv") | Read CSV file | |
pd.read_excel("file.xlsx") | Read Excel file | |
pd.read_json("file.json") | Read JSON file | |
pd.read_sql(query, connection) | Read from SQL database | |
pd.read_html(url) | Read HTML tables |
Writing Data
| Command | Copy | Description |
|---|---|---|
df.to_csv("file.csv") | Write to CSV file | |
df.to_excel("file.xlsx") | Write to Excel file | |
df.to_json("file.json") | Write to JSON file | |
df.to_sql("table", connection) | Write to SQL database |
Creating DataFrames
| Command | Copy | Description |
|---|---|---|
pd.DataFrame(data) | Create DataFrame from dict/list | |
pd.DataFrame(data, columns=["A", "B"]) | Create DataFrame with column names | |
pd.Series([1, 2, 3]) | Create Series |
Viewing Data
| Command | Copy | Description |
|---|---|---|
df.head() | View first 5 rows | |
df.tail() | View last 5 rows | |
df.sample(5) | View random 5 rows | |
df.info() | Get DataFrame info | |
df.describe() | Get statistical summary | |
df.shape | Get dimensions (rows, columns) | |
df.columns | Get column names | |
df.dtypes | Get data types | |
df.index | Get index |
Selection
| Command | Copy | Description |
|---|---|---|
df["column"] | Select single column | |
df[["col1", "col2"]] | Select multiple columns | |
df.loc[row_label] | Select by label | |
df.iloc[row_index] | Select by position | |
df.loc[0:5, "column"] | Select rows and column by label | |
df.iloc[0:5, 0:2] | Select rows and columns by position |
Filtering
| Command | Copy | Description |
|---|---|---|
df[df["col"] > 5] | Filter rows by condition | |
df[(df["col1"] > 5) & (df["col2"] < 10)] | Filter with multiple conditions (AND) | |
df[(df["col1"] > 5) | (df["col2"] < 10)] | Filter with multiple conditions (OR) | |
df[df["col"].isin([1, 2, 3])] | Filter by values in list | |
df[df["col"].str.contains("text")] | Filter by string contains |
Sorting
| Command | Copy | Description |
|---|---|---|
df.sort_values("column") | Sort by column (ascending) | |
df.sort_values("column", ascending=False) | Sort descending | |
df.sort_values(["col1", "col2"]) | Sort by multiple columns | |
df.sort_index() | Sort by index |
Adding/Removing Columns
| Command | Copy | Description |
|---|---|---|
df["new_col"] = values | Add new column | |
df.drop("column", axis=1) | Drop column | |
df.drop(["col1", "col2"], axis=1) | Drop multiple columns | |
df.rename(columns={"old": "new"}) | Rename column |
Adding/Removing Rows
| Command | Copy | Description |
|---|---|---|
df.drop(index) | Drop row by index | |
pd.concat([df1, df2]) | Concatenate DataFrames | |
df.append(other_df) | Append rows |
Handling Missing Data
| Command | Copy | Description |
|---|---|---|
df.isna() | Check for missing values | |
df.isnull() | Check for null values | |
df.dropna() | Drop rows with missing values | |
df.fillna(value) | Fill missing values | |
df.fillna(method="ffill") | Forward fill missing values | |
df.fillna(method="bfill") | Backward fill missing values |
GroupBy
| Command | Copy | Description |
|---|---|---|
df.groupby("column").mean() | Group by and calculate mean | |
df.groupby("column").sum() | Group by and calculate sum | |
df.groupby("column").count() | Group by and count | |
df.groupby(["col1", "col2"]).mean() | Group by multiple columns | |
df.groupby("column").agg(["mean", "sum"]) | Multiple aggregations |
Merge/Join
| Command | Copy | Description |
|---|---|---|
pd.merge(df1, df2, on="key") | Merge on column | |
pd.merge(df1, df2, left_on="key1", right_on="key2") | Merge on different columns | |
pd.merge(df1, df2, how="left") | Left join | |
pd.merge(df1, df2, how="right") | Right join | |
pd.merge(df1, df2, how="outer") | Outer join | |
pd.merge(df1, df2, how="inner") | Inner join |
Apply Functions
| Command | Copy | Description |
|---|---|---|
df["col"].apply(func) | Apply function to column | |
df.apply(func) | Apply function to DataFrame | |
df["col"].map(dict) | Map values using dictionary | |
df.applymap(func) | Apply function to each element |
String Operations
| Command | Copy | Description |
|---|---|---|
df["col"].str.lower() | Convert to lowercase | |
df["col"].str.upper() | Convert to uppercase | |
df["col"].str.strip() | Remove whitespace | |
df["col"].str.replace("old", "new") | Replace string | |
df["col"].str.split(",") | Split string | |
df["col"].str.contains("text") | Check if contains text |
Date/Time
| Command | Copy | Description |
|---|---|---|
pd.to_datetime(df["col"]) | Convert to datetime | |
df["date"].dt.year | Extract year | |
df["date"].dt.month | Extract month | |
df["date"].dt.day | Extract day | |
df["date"].dt.dayofweek | Get day of week |
Statistics
| Command | Copy | Description |
|---|---|---|
df["col"].mean() | Calculate mean | |
df["col"].median() | Calculate median | |
df["col"].std() | Calculate standard deviation | |
df["col"].var() | Calculate variance | |
df["col"].min() | Get minimum value | |
df["col"].max() | Get maximum value | |
df["col"].sum() | Calculate sum | |
df["col"].count() | Count non-null values | |
df["col"].value_counts() | Count unique values | |
df["col"].unique() | Get unique values | |
df["col"].nunique() | Count unique values |
Pivot Tables
| Command | Copy | Description |
|---|---|---|
df.pivot_table(values="val", index="idx", columns="col") | Create pivot table | |
pd.crosstab(df["col1"], df["col2"]) | Create cross-tabulation |
Reset Index
| Command | Copy | Description |
|---|---|---|
df.reset_index() | Reset index to default | |
df.reset_index(drop=True) | Reset index and drop old | |
df.set_index("column") | Set column as index |
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