pandas library

pandas is an open-source project, funded by nonprofit NumFOCUS.

pandas (imported as pd) is Python's standard library for tabular data — rows and columns, like a spreadsheet, with tools for filtering, sorting, and summarizing built in. It's a third-party package, not part of the standard library, and is built on top of NumPy.

Install

pip install pandas

Import

pd is the near-universal alias for pandas — used throughout this page and in virtually every codebase that imports it.

import pandas as pd

Type	Shape	Like
`Series`	One column of labeled values	A single spreadsheet column
`DataFrame`	Many columns, sharing an index	A whole spreadsheet or table

Building a DataFrame

A DataFrame is most often built from a list of dicts — one dict per row, with matching keys becoming the column names.

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5, "venomous": False},
    {"species": "burmese", "length_ft": 12, "venomous": False},
])

print(snakes)

Building from a dict of lists

The same table can also be built from a single dict where each key maps to a whole column's worth of values. The rows line up by position across every list, so all the lists need to be the same length.

pd.DataFrame({
    "species": ["ball", "burmese"],
    "length_ft": [4.5, 12],
})

Run a building a DataFrame example

All the examples above, combined into one script:

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5, "venomous": False},
    {"species": "burmese", "length_ft": 12, "venomous": False},
])

print(snakes)

import pandas as pd

snakes = pd.DataFrame({
    "species": ["ball", "burmese"],
    "length_ft": [4.5, 12],
})

print(snakes)

Working with a DataFrame

A single column pulled out of a DataFrame (with df["column"]) is a Series — comparing it to a value produces a boolean mask, exactly like a NumPy array, for filtering rows.

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5},
    {"species": "burmese", "length_ft": 12},
    {"species": "boa", "length_ft": 8},
])

big_snakes = snakes[snakes["length_ft"] > 5]
print(big_snakes)

Sorting rows

Returns the DataFrame reordered by a column. .sort_values("column") sorts ascending by default, or descending with ascending=False. Like most pandas operations, it returns a new DataFrame rather than reordering the original in place.

snakes.sort_values("length_ft", ascending=False)    # rows reordered longest-first

Summarizing a column

Calling .mean(), .max(), or similar directly on a column summarizes it down to a single number. The same way it would on a NumPy array — a Series supports the same aggregation methods.

snakes["length_ft"].mean()    # 8.166666666666666
snakes["length_ft"].max()     # 12.0

Run a working with a DataFrame example

All the examples above, combined into one script:

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5},
    {"species": "burmese", "length_ft": 12},
    {"species": "boa", "length_ft": 8},
])

big_snakes = snakes[snakes["length_ft"] > 5]
print(big_snakes)

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5},
    {"species": "burmese", "length_ft": 12},
    {"species": "boa", "length_ft": 8},
])

print(snakes.sort_values("length_ft", ascending=False))

import pandas as pd

snakes = pd.DataFrame([
    {"species": "ball", "length_ft": 4.5},
    {"species": "burmese", "length_ft": 12},
    {"species": "boa", "length_ft": 8},
])

print(snakes["length_ft"].mean())
print(snakes["length_ft"].max())