Master programming syntax, solve interactive challenges & run live code in Syntaxis Explore Arena →

menu_book Course Curriculum (Pandas) expand_more

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 / Time Series & I/O

to_datetime()

Learn to_datetime() in Pandas with a dedicated explanation, a concept-specific example, practical cautions, and a mastery check.

12 min read • Advanced

pandas-041.py

python

import pandas as pd
s=pd.Series(['2026-09-01','2026-09-15'])
print(pd.to_datetime(s, format='%Y-%m-%d'))

What to watch

Content source & attribution

Original Syntaxis Content · Original Syntaxis Content

Lesson quizzes

to_datetime() Mastery Check →

stack() and unstack()