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. |