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Series & DataFrames

Series construction DataFrame construction index and columns dtypes inspection head(), tail(), sample() assign() new columns rename() labels astype() conversion

Selection & Filtering

column selection loc[] label selection iloc[] positional selection Boolean filtering query() isin() between() where() and mask()

Cleaning Data

isna() and notna() dropna() fillna() duplicated() and drop_duplicates() replace() string strip and normalize to_numeric() coercion category dtype

Grouping & Aggregation

groupby() basics multiple aggregations with agg() named aggregation transform() filter() groups value_counts() pivot_table() crosstab()

Joining & Reshaping

merge() inner join left merge join() on index concat() rows concat() columns melt() pivot() stack() and unstack()

Time Series & I/O

to_datetime() DatetimeIndex resample() rolling() shift() read_csv() read_json() to_csv()

Pandas / Grouping & Aggregation

named aggregation

Learn named aggregation in Pandas with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Intermediate

pandas-027.py

python

import pandas as pd
df=pd.DataFrame({'store':['N','N','S'],'sales':[10,20,15]})
print(df.groupby('store').agg(total_sales=('sales','sum'), avg_sales=('sales','mean')))

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

named aggregation Mastery Check →

multiple aggregations with agg()