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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 / Joining & Reshaping

left merge

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

12 min read • Advanced

pandas-034.py

python

import pandas as pd
customers=pd.DataFrame({'id':[1,2,3]})
plans=pd.DataFrame({'customer_id':[1,3],'plan':['pro','basic']})
print(customers.merge(plans,left_on='id',right_on='customer_id',how='left'))

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

left merge Mastery Check →

merge() inner join

join() on index