How to Use Pandas in Python

What you’ll build or solve

You’ll load real data into a Pandas DataFrame, inspect it, clean it lightly, and answer simple questions with filters and groupings.

When this approach works best

This approach works best when you:

• Work with tabular data like CSV exports from spreadsheets, payments, analytics, or logs.
• Need to filter and summarize data fast, like “top countries by signups” or “average order value by month”.
• Want a repeatable script that turns raw data into a cleaned report.

Avoid this approach when:

• You only have a handful of values. A plain list or dictionary may be simpler.
• Your file is huge and does not fit in memory. You may need chunked processing or a database.

Prerequisites

• Python installed
• Pandas installed (python -m pip install pandas)
• You know what variables are
• You know what lists and dictionaries are
• You know what a CSV file is

Step-by-step instructions

1) Import Pandas and load a CSV into a DataFrame

Most Pandas work starts by reading a file into a DataFrame.

import pandas as pd

df = pd.read_csv("data.csv")
print(df.head())

What to look for:

head() prints the first few rows so you can confirm the file loaded and columns look right.

If your CSV uses a different delimiter:

df = pd.read_csv("data.csv", sep=";")

2) Inspect columns, types, and missing values

Before you filter or calculate, check the shape, types, and nulls.

print(df.shape)  # (rows, columns)
print(df.columns)  # column names
print(df.dtypes)  # types per column
print(df.isna().sum())  # missing values per column

For a compact summary:

print(df.info())

3) Select columns and filter rows

Select columns by passing a list of column names.

subset = df[["country", "signups", "source"]]
print(subset.head())

Filter rows with a boolean condition.

us = df[df["country"] == "US"]
print(us.head())

Combine conditions with & and wrap each condition in parentheses.

paid_us = df[(df["country"] == "US") & (df["plan"] == "paid")]
print(paid_us.head())

4) Convert types and handle missing values

Convert columns when the type does not match how you want to use the data.

df["revenue"] = pd.to_numeric(df["revenue"], errors="coerce")
df["date"] = pd.to_datetime(df["date"], errors="coerce")

If you want to treat missing numeric values as zero:

df["revenue"] = df["revenue"].fillna(0)

What to look for:

• errors="coerce" turns bad values into NaN instead of crashing.
• fillna(0) replaces missing values with zero.

5) Create new columns with calculations

Build new columns using existing columns.

df["revenue_per_signup"] = df["revenue"] / df["signups"]
print(df[["revenue", "signups", "revenue_per_signup"]].head())

If signups can be zero:

df["revenue_per_signup"] = (
    df["revenue"] / df["signups"].replace(0, pd.NA)
)

What to look for:

replace(0, pd.NA) avoids dividing by zero.

6) Group and summarize data

Grouping helps you get totals or averages by category.

Total signups by country:

by_country = df.groupby("country", as_index=False)["signups"].sum()
print(by_country.sort_values("signups", ascending=False).head(10))

Multiple metrics at once:

summary = df.groupby("source", as_index=False).agg(
    signups=("signups", "sum"),
    revenue=("revenue", "sum"),
    avg_revenue=("revenue", "mean"),
)

print(summary.sort_values("revenue", ascending=False))

What to look for:

Each line uses (column_name, aggregation_function) to define what to calculate.

7) Save results back to CSV

Once you have a filtered DataFrame or summary table, write it out.

by_country.to_csv("signups_by_country.csv", index=False)

What to look for:

index=False prevents Pandas from adding an extra index column.

Examples you can copy

1) Basic “load and inspect” script

import pandas as pd

df = pd.read_csv("data.csv")

print("rows, cols:", df.shape)
print("columns:", list(df.columns))
print(df.head(5))
print("missing values:")
print(df.isna().sum())

2) Filter rows and keep only needed columns

import pandas as pd

df = pd.read_csv("data.csv")

filtered = df[df["country"].isin(["US", "CA", "GB"])]
filtered = filtered[["date", "country", "source", "signups", "revenue"]]

filtered.to_csv("filtered.csv", index=False)

3) Simple report: top sources by revenue

import pandas as pd

df = pd.read_csv("data.csv")

df["revenue"] = pd.to_numeric(df["revenue"], errors="coerce").fillna(0)
df["signups"] = pd.to_numeric(df["signups"], errors="coerce").fillna(0)

report = df.groupby("source", as_index=False).agg(
    revenue=("revenue", "sum"),
    signups=("signups", "sum"),
)

report["revenue_per_signup"] = (
    report["revenue"] / report["signups"].replace(0, pd.NA)
)

report = report.sort_values("revenue", ascending=False)
report.to_csv("report_by_source.csv", index=False)

print(report.head(10))

Common mistakes and how to fix them

Mistake 1: Using parentheses instead of brackets

You might write:

print(df("country"))

Why it breaks:

Use square brackets df["column"], not parentheses.

Correct approach:

print(df["country"])
print(df[["country", "source"]])

Mistake 2: Using and / or instead of & / |

You might write:

paid_us = df[(df["country"] == "US") and (df["plan"] == "paid")]

Why it breaks:

and tries to evaluate the whole column as one True/False value.

Correct approach:

paid_us = df[(df["country"] == "US") & (df["plan"] == "paid")]

Mistake 3: Getting a KeyError due to column name mismatch

You might write:

df["Country"]

Why it breaks:

Column names are case-sensitive.

Correct approach:

print(df.columns)
df["country"]

Troubleshooting

If you see ModuleNotFoundError: No module named 'pandas', install it:

python -m pip install pandas

If you see garbled characters, pass an encoding:

df = pd.read_csv("data.csv", encoding="utf-8")

If numbers load as text (object type), convert them:

df["revenue"] = pd.to_numeric(df["revenue"], errors="coerce")

If your filter gives “truth value of a Series is ambiguous,” you likely used and/or. Use &/| and parentheses.

If your output CSV has an extra first column, add index=False to to_csv().

Quick recap

• Load data with pd.read_csv() into a DataFrame.
• Inspect with head(), info(), and isna().sum().
• Select columns with df[["col1", "col2"]] and filter rows with boolean masks.
• Convert types with pd.to_numeric() and pd.to_datetime(), use fillna() when needed.
• Create new columns with math on columns and handle divide-by-zero if needed.
• Summarize with groupby().agg() and save with to_csv(index=False).