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General Functions

General functions are the most commonly used functions in Pandas. They include functions for data manipulation, data cleaning, data merging, and more.

Data Manipulations

Function Description
pd.concat Concatenate pandas objects along a particular axis with optional set logic along the other axes.
pd.crosstab Compute a simple cross-tabulation of two (or more) factors.
pd.cut Bin values into discrete intervals.
pd.qcut Quantile-based discretization function.
pd.get_dummies Convert categorical variable into dummy/indicator variables.
pd.merge Merge DataFrame or named Series objects with a database-style join.
pd.pivot Reshape data (produce a “pivot” table) based on column values.
pd.pivot_table Create a spreadsheet-style pivot table as a DataFrame.
pd.unique Hash table-based unique.

Top Level Missing Data

Function Description
pd.isna Detect missing values.
pd.notna Detect existing (non-missing) values.
pd.isnull Detect missing values.
pd.notnull Detect existing (non-missing) values.

Top Level Conversions

Function Description
pd.to_numeric Convert argument to a numeric type.