Filtering · Pandas · CodeMIRAI

Filtering

2 min read 5 runnable examples Quiz included

Filtering rows with a condition

A comparison on a column produces True/False for every row. Put it in brackets to keep only the True rows:

A condition on a column gives one True or False per row; indexing with it keeps the True rows. Combine conditions with & and |, in brackets.

Python

import pandas as pd

people = pd.DataFrame({
    "name": ["Asha", "Leo", "Mei", "Raj", "Sofia"],
    "age": [21, 30, 24, 35, 28],
    "city": ["Mumbai", "London", "Osaka", "Delhi", "Rome"],
})

print(people["age"] > 25)          # a boolean Series
print(people[people["age"] > 25])  # rows where it is True

Combining conditions

Use & (and), | (or) and ~ (not), with brackets around each condition:

Python

import pandas as pd

people = pd.DataFrame({
    "name": ["Asha", "Leo", "Mei", "Raj", "Sofia"],
    "age": [21, 30, 24, 35, 28],
    "country": ["India", "UK", "Japan", "India", "Italy"],
})
print(people[(people["age"] > 22) & (people["country"] == "India")])
print(people[(people["age"] < 22) | (people["age"] > 32)])

isin and string methods

Python

import pandas as pd

people = pd.DataFrame({
    "name": ["Asha Verma", "Leo Kim", "Mei Tanaka", "Raj Patel"],
    "country": ["India", "UK", "Japan", "India"],
})
print(people[people["country"].isin(["India", "Japan"])])
print(people[people["name"].str.contains("a ")])
print(people[people["name"].str.startswith("M")])

.str gives a column all the Python string methods, applied to every row.

query(): conditions as text

Python

import pandas as pd

products = pd.DataFrame({"name": ["Mouse", "Monitor", "Lamp", "Chair"], "price": [24.5, 279, 27.75, 349], "stock": [120, 15, 48, 8]})
print(products.query("price < 100 and stock > 20"))

loc and iloc

loc selects by labels (row index labels and column names),
iloc selects by integer positions.

Python

import pandas as pd

df = pd.DataFrame({"name": ["Asha", "Leo", "Mei"], "age": [21, 30, 24], "city": ["Mumbai", "London", "Osaka"]})
print(df.loc[0, "name"])                    # row label 0, column "name"
print(df.loc[df["age"] > 22, ["name", "city"]])   # filter rows AND pick columns
print(df.iloc[1])                           # second row
print(df.iloc[:2, :2])                      # first 2 rows, first 2 columns

df.loc[condition, columns] is the clearest way to filter and select in one step.

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