Pandas Cheat Sheet

Pandas Quick Reference

1. Import Pandas

import pandas as pd
  

2. Reading Data

Load CSV files into a DataFrame:

df = pd.read_csv("data.csv")          # read file
df = pd.read_csv("data.csv", index_col=0)  # use first column as index
  

3. Inspecting Data

• Check dimensions:

  df.shape    # (rows, columns)
        

• View top/bottom rows:

  df.head()   # first 5 rows
  df.tail()   # last 5 rows
        

• Column names:

  df.columns
        

• Quick summary:

  df.info()   # types, non-null counts
  df.describe()  # summary statistics for numeric columns
        

4. Data Types (dtypes)

Check the type of a column or all columns:

df["price"].dtype   # single column
df.dtypes           # all columns
  

Convert types:

df["points"].astype("float64")
  

5. Index

df.index.dtype
df.index
  

6. Missing Data (NaN)

Detect missing values:

pd.isnull(df["country"])   # True if NaN
pd.notnull(df["country"])  # True if not NaN
  

Fill or replace missing values:

df["region_2"].fillna("Unknown")       # replace NaN
df["region_2"].fillna(method="bfill")  # backfill
df["twitter_handle"].replace("@old","@new")
  

7. Core Objects

DataFrame: table of rows & columns

pd.DataFrame({"Yes":[50,21], "No":[131,2]})
  

Series: single column or list of values

pd.Series([1,2,3,4], index=["A","B","C","D"], name="Numbers")
  

Summary

Workflow: Read & inspect → check dtypes → handle NaN → manipulate/convert → analyze.