Column operations

DataFrame columns are accessed with brackets and statistics are computed on them.

Selecting and adding columns

A single column is selected with df["ad"], several with df[["ad", "yas"]]. A new column is added by computing it from existing ones:

df["yillik"] = df["maas"] * 12
df["genc"] = df["yas"] < 30

The operation is applied to the whole column at once; it is the same as vectorisation in NumPy.

Dropping and renaming

Column operations return a new DataFrame; the original table doesn't change:

df2 = df.drop(columns=["genc"])
df3 = df.rename(columns={"maas": "ucret"})
print(df["maas"].sum())   # column total

To use the result, assign it to a new variable or write df = df.drop(...).

Exercise Computed column

Add a yillik column to df equal to 12 times maas. Print the total of this column.

Show example solution

import pandas as pd

df = pd.DataFrame({
    "ad": ["Ali", "Ece", "Can", "Su"],
    "yas": [34, 28, 45, 22],
    "maas": [5000, 4200, 7100, 3000],
})

df["yillik"] = df["maas"] * 12
print(df["yillik"].sum())

Test yourself

What is the difference between <code>df[["ad"]]</code> and <code>df["ad"]</code>?
- There is no difference
- The first errors
- The second returns a list
- The first returns a one-column DataFrame, the second a Series
Show answer

The first returns a one-column DataFrame, the second a Series — Giving names inside a list always returns a DataFrame.

Remove the maas column from the table.

df2 = df.____(columns=["maas"])

Show answer

df2 = df.drop(columns=["maas"])

Match the expressions.

df["yas"]

Series
df[["yas", "ad"]]

Two-column table
df.rename

Renaming
df["y"] = ...

New column

Next lesson: Selecting and filtering data

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