Pandas is one of the most important Python libraries for data analysis but remembering everything is hard.

That’s why analysts rely on cheat sheets.

This Pandas cheat sheet covers the most-used commands in real data analysis work, not rare edge cases. If you master what’s below, you’ll handle most day-to-day data tasks with confidence.

Importing Pandas

import pandas as pd

Loading Data

df = pd.read_csv("data.csv")
df = pd.read_excel("data.xlsx")
df = pd.read_json("data.json")

Viewing Data

df.head()
df.tail()
df.sample(5)
df.info()
df.describe()

These help you quickly understand structure, size, and data types.

Selecting Columns

df["column"]
df[["col1", "col2"]]

Selecting Rows

df.loc[0]
df.iloc[0]
df.loc[df["sales"] > 1000]

Filtering Data

df[df["region"] == "Europe"]
df[(df["sales"] > 500) & (df["profit"] > 50)]

Sorting Data

df.sort_values("sales")
df.sort_values("sales", ascending=False)

Handling Missing Values

df.isna()
df.dropna()
df.fillna(0)

Missing data handling is one of the most common real-world tasks.

Renaming Columns

df.rename(columns={"old_name": "new_name"})
df.columns = df.columns.str.lower()

Creating New Columns

df["revenue"] = df["price"] * df["quantity"]

Grouping & Aggregation

df.groupby("region")["sales"].sum()
df.groupby("category").agg(
    total_sales=("sales", "sum"),
    avg_profit=("profit", "mean")
)

This is where Pandas replaces many Excel pivot tables.

Value Counts

df["status"].value_counts()

Perfect for quick distributions.

Removing Duplicates

df.drop_duplicates()

Changing Data Types

df["date"] = pd.to_datetime(df["date"])
df["price"] = df["price"].astype(float)

Merging DataFrames

pd.merge(df1, df2, on="id", how="inner")
pd.merge(df1, df2, on="id", how="left")

Applying Functions

df["profit_margin"] = df["profit"] / df["revenue"]
df["sales_level"] = df["sales"].apply(lambda x: "High" if x > 1000 else "Low")

Basic String Operations

df["name"].str.lower()
df["email"].str.contains("@")

Exporting Data

df.to_csv("output.csv", index=False)
df.to_excel("output.xlsx", index=False)

Common Pandas Mistakes Beginners Make

• Forgetting to assign results back to the DataFrame
• Mixing loc and iloc
• Ignoring data types
• Overusing loops instead of vectorized operations

How Analysts Use Pandas in Real Jobs

Pandas is commonly used for:

• data cleaning
• exploratory analysis
• feature creation
• report preparation
• feeding dashboards and ML models

You don’t need to memorize every Pandas function.

If you understand:

• loading data
• filtering
• grouping
• cleaning

You’re already job-ready for most entry-level data analysis tasks.

Bookmark this cheat sheet,you’ll come back to it often.

FAQs