Created
Sep 1, 2025 10:14 PM
Multi-select
Status
Not started
1. Reshaping & Pivoting Data
pd.melt()- Unpivot data from wide to long formatdf.pivot()- Reshape data (index to columns)pd.pivot_table()- Create a pivot table with aggregationpd.crosstab()- Compute a cross-tabulation (frequency table)pd.wide_to_long()- Convert wide-format data to long-format
2. Combining & Merging Data
pd.merge()- Merge DataFrames (SQL-style join)pd.merge_ordered()- Merge with ordered data (like SQL outer join)pd.merge_asof()- Merge based on nearest key (as of join)pd.concat()- Concatenate DataFrames along an axis
3. Handling Categorical & Dummy Data
pd.get_dummies()- Convert categorical variables into dummy/indicator variablespd.from_dummies()- Convert dummy variables back to categoricaldf['col'].factorize()- Encode categorical data as numerical labelsdf['col'].unique()- Return unique valuespd.cut(df['col'], bins)- Bin values into discrete intervalspd.qcut(df['col'], q)- Bin values into quantile-based intervals
4. Missing Data Handling
df['col'].isna()/df['col'].isnull()- Detect missing valuesdf['col'].notna()/df['col'].notnull()- Detect non-missing values
5. Type Conversion & Parsing
pd.to_numeric(df['col'])- Convert to numeric dtypepd.to_datetime(df['col'])- Convert to datetimepd.to_timedelta(df['col'])- Convert to timedeltapd.eval()- Evaluate string expressions
6. Date & Time Handling
pd.date_range()- Generate date-time rangepd.bdate_range()- Generate business day rangepd.period_range()- Generate period rangepd.timedelta_range()- Generate timedelta rangedf['datetime_col'].infer_freq()- Infer frequencypd.interval_range()- Generate interval rangespd.tseries.api.guess_datetime_format()- Guess datetime format
7. Hashing & Interoperability
pd.util.hash_array()- Hash a NumPy arraypd.util.hash_pandas_object()- Hash a pandas objectpd.api.interchange.from_dataframe()- Convert to interchange object
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