DataFrames

2 min read 5 runnable examples Quiz included

Creating a DataFrame

The most common ways are from a dictionary of columns, or from a list of row dictionaries:

A DataFrame is a table with labelled rows (the index) and labelled columns; each column on its own is a Series.

Python

import pandas as pd

by_columns = pd.DataFrame({
    "name": ["Asha", "Leo", "Mei"],
    "age": [21, 30, 24],
})

by_rows = pd.DataFrame([
    {"name": "Asha", "age": 21},
    {"name": "Leo", "age": 30},
    {"name": "Mei", "age": 24},
])
print(by_columns.equals(by_rows))
print(by_rows)

Reading CSV data

Real data usually arrives as a CSV file: pd.read_csv("sales.csv"). Here we read CSV text directly:

Python

import pandas as pd
from io import StringIO

csv = """order_id,customer,product,quantity,price
101,Asha,Keyboard,1,89.99
102,Leo,Monitor,2,279.00
103,Asha,SSD,1,109.00
104,Mei,Headphones,1,199.00
105,Raj,Mouse,4,24.50
"""
orders = pd.read_csv(StringIO(csv))
print(orders)

First look at any dataset

Four commands you'll run on every new dataset:

Python

import pandas as pd
from io import StringIO

csv = """order_id,customer,product,quantity,price
101,Asha,Keyboard,1,89.99
102,Leo,Monitor,2,279.00
103,Asha,SSD,1,109.00
104,Mei,Headphones,1,199.00
105,Raj,Mouse,4,24.50
"""
orders = pd.read_csv(StringIO(csv))
print(orders.shape)          # (rows, columns)
print(orders.head(2))        # first rows
orders.info()                # column names, types, missing values
print(orders.describe())     # statistics for numeric columns

Selecting columns

Python

import pandas as pd

df = pd.DataFrame({"name": ["Asha", "Leo"], "age": [21, 30], "city": ["Mumbai", "London"]})
print(df["name"])                 # one column: a Series
print(df[["name", "city"]])       # several columns: a DataFrame (note the double brackets)
print(df.columns.tolist())

Adding a column

Python

import pandas as pd

orders = pd.DataFrame({"product": ["Keyboard", "Monitor", "Mouse"], "quantity": [1, 2, 4], "price": [89.99, 279.0, 24.5]})
orders["total"] = orders["quantity"] * orders["price"]
orders["big_order"] = orders["total"] > 200
print(orders)

Column arithmetic is vectorised, just like NumPy: one line computes every row.

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