Pandas Function
Explore DataFrame
| To get the DataFrame Columns | df.columns |
| To get the rows and column count of DataFrame | df.shape |
| To get the DataFrame Column and Datatype | df.dtypes |
| To get the DataFrame first 5 rows | df.head() |
Selection
| To get the DataFrame first 10 rows using iloc: [ : , :] before comma rows and after comma columns | df.iloc[:10] |
| To get the DataFrame first 5 Columns using iloc: | df.iloc[ : , :5] |
| To get columns by name: loc – label based | df.loc[:,[“column1″,”column2″,”column3”]] |
| To get random 10% rows | df.sample(frac = 0.1) |
| To get 50 rows | df.sample(n = 50) |
DataFrame Column Manipulation
| Rename column by position: here 3 rd column renamed | df.rename( ) df.rename(columns={‘col1′:’column1’, ‘col2′:’column2’ }, inplace = True) |
| rename columns having underscore ‘_’ in their names with ‘dot’ | df.columns = df.columns.str.replace(‘_’ , ‘.’) |
| rename column by adding prefix and suffix | df = df.add_prefix(‘de_’) df = df.add_prefix(‘_lv’) |
| Selecting a column as index | df.set_index( ) |
DataFrame Data Manipulation
| Removing rows or columns | df.drop() |
| sort | df.sort_values( ) |
| grouping | df.groupby( ) |
| Filter | df.query( ) or df.where( ) |
| Find missing Values | df.isnull( ) |
| Drop missing Values | df.dropna( ) |
| Drop duplicates | df.drop_duplicates( ) |
| Rank | df.rank() |
| Selecting column by types | df.select_dtypes( ) |
| Concatenate 2 dataframe | pd.concat |
| Top Level Function | |
| concat 2 dataframe | pd.concat() |
| Merging (Joining) based on common values | pd.merge( ) |