Python Pandas DataFrame

A DataFrame is a table with labeled columns, like a spreadsheet inside your program. You can edit it, sort it, fill in blanks, and combine tables.

In this page:

Editing Columns
Handling Missing Data
Sorting DataFrames
Grouping Data
Merging and Joining

Syntax

python

import pandas as pd

df = pd.DataFrame({"column1": [a, b], "column2": [c, d]})
df["column"]
df.loc[row_label]

Editing Columns

Adding a new column is as simple as assigning to a new key, like df[total] = df[price] * df[qty], which computes element-wise across every row at once.

Modifying an existing column works the same way, and df.drop(columns=[...]) removes columns you no longer need.

Example: Editing Columns

python

import pandas as pd
df = pd.DataFrame({"price": [10, 20], "qty": [2, 3]})
df["total"] = df["price"] * df["qty"]  # new column computed element-wise
print(df)

Handling Missing Data

Real-world data is rarely complete: df.isna() flags missing values, df.dropna() removes rows or columns containing them, and df.fillna(value) replaces them with a specified value or a computed statistic like the column mean.

Choosing between dropping and filling depends on whether the missing data is rare enough to discard safely.

Example: Handling Missing Data

python

import pandas as pd
import numpy as np
df = pd.DataFrame({"value": [1, np.nan, 3]})  # np.nan represents a missing value
print(df.fillna(0))  # replaces missing values with 0

Sorting DataFrames

df.sort_values(column) reorders the DataFrame's rows based on the values in one or more specified columns, ascending by default, with a ascending=False option for descending order.

This is the DataFrame equivalent of Python's sorted(), but operating over full labeled rows instead of standalone values.

Example: Sorting DataFrames

python

import pandas as pd
df = pd.DataFrame({"score": [70, 90, 50]})
print(df.sort_values("score"))  # rows reordered ascending by score

Grouping Data

df.groupby(column) splits the DataFrame into groups sharing the same value in that column, and chaining an aggregation like .mean() or .sum() afterward computes that statistic separately within each group.

This split-apply-combine pattern is the core tool for summarizing data by category.

Example: Grouping Data

python

import pandas as pd
df = pd.DataFrame({"team": ["A", "A", "B"], "score": [10, 20, 30]})
print(df.groupby("team").mean())  # average score computed separately per team

Merging and Joining

pd.concat() stacks multiple DataFrames together (by rows or columns), while pd.merge() joins them based on matching values in a shared key column, similar to a SQL join.

Merging is essential whenever related data is split across multiple tables that need to be combined for analysis.

Example: Merging and Joining

python

import pandas as pd
a = pd.DataFrame({"id": [1, 2], "name": ["Alex", "Sam"]})
b = pd.DataFrame({"id": [1, 2], "score": [90, 80]})
print(pd.merge(a, b, on="id"))  # joins the two tables on the shared id column

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Python Pandas DataFrame

Editing Columns

Handling Missing Data

Sorting DataFrames

Grouping Data

Merging and Joining

Chapter Quiz — Complete all 14 topics to unlock

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