$devvkit learn --librarie pandas-guide

Pandas Guide

[python][data][analysis]

Python

Install

pip install pandas

Core data structure: DataFrame: a labeled, two-dimensional table with typed columns.

Handles real-world data messiness: NaN, inconsistent formats, duplicates.

Never loop over rows: use vectorized operations with apply(), groupby(), boolean indexing.

Creating DataFrames

Create DF· From dictionary.

import pandas as pd
df = pd.DataFrame({'name': ['Alice', 'Bob'], 'age': [30, 25]})

Read CSV· Load CSV file.

df = pd.read_csv('data.csv')
df = pd.read_csv('data.csv', nrows=100)

Write CSV· Save to CSV.

df.to_csv('output.csv', index=False)

Inspect· View data.

df.head(10)
df.info()
df.describe()

Select columns· Choose columns.

df['name']
df[['name', 'age']]

Filter rows· Condition.

df[df['age'] > 25]
df[(df['age'] > 25) & (df['city'] == 'NYC')]

Query· SQL-like filter.

df.query('age > 25 and city == 'NYC'')

Add column· New column.

df['age_group'] = df['age'].apply(lambda x: 'young' if x < 30 else 'adult')

Merge· Join DataFrames.

pd.merge(df1, df2, on='user_id', how='left')

Group by· Aggregate.

df.groupby('city').agg({'age': ['mean', 'count', 'max']})