General Functions

Modify columns

expand_column(df, column_name, sep[, concat])	Expand a categorical column with multiple labels into dummy-coded columns.
concatenate_columns(df, column_names, …[, sep])	Concatenates the set of columns into a single column.
deconcatenate_column(df, column_name[, sep, …])	De-concatenates a single column into multiple columns.
remove_columns(df, column_names)	Remove the set of columns specified in column_names.
add_column(df, column_name, value[, …])	Add a column to the dataframe.
add_columns(df[, fill_remaining])	Add multiple columns to the dataframe.
transform_column(df, column_name, function)	Transform the given column in-place using the provided function.
transform_columns(df, column_names, function)	Transform multiple columns through the same transformation.
rename_column(df, old_column_name, …)	Rename a column in place.
rename_columns(df, new_column_names)	Rename columns in place.
reorder_columns(df, column_order)	Reorder DataFrame columns by specifying desired order as list of col names.
collapse_levels(df[, sep])	Flatten multi-level column dataframe to a single level.
change_type(df, column_name, dtype[, …])	Change the type of a column.
limit_column_characters(df, column_length[, …])	Truncate column sizes to a specific length.
row_to_names(df[, row_number, remove_row, …])	Elevates a row to be the column names of a DataFrame.
clean_names(df[, strip_underscores, …])	Clean column names.
currency_column_to_numeric(df, column_name)	Convert currency column to numeric.
groupby_agg(df, by, new_column_name, …[, …])	Shortcut for assigning a groupby-transform to a new column.
join_apply(df, func, new_column_name)	Join the result of applying a function across dataframe rows.
drop_duplicate_columns(df, column_name[, …])	Remove a duplicated column specified by column_name, its index.
process_text(df, column_name[, …])	Apply a Pandas string method to an existing column and return a dataframe.

Modify values

fill_empty(df, column_names, value)	Fill NaN values in specified columns with a given value.
fill_direction(df[, directions, limit])	Provide a method-chainable function for filling missing values in selected columns.
convert_excel_date(df, column_name)	Convert Excel’s serial date format into Python datetime format.
convert_matlab_date(df, column_name)	Convert Matlab’s serial date number into Python datetime format.
convert_unix_date(df, column_name)	Convert unix epoch time into Python datetime format.
remove_empty(df)	Drop all rows and columns that are completely null.
coalesce(df, column_names[, …])	Coalesce two or more columns of data in order of column names provided.
find_replace(df[, match])	Perform a find-and-replace action on provided columns.
round_to_fraction(df[, column_name, …])	Round all values in a column to a fraction.
update_where(df, conditions, …)	Add multiple conditions to update a column in the dataframe.
to_datetime(df, column_name, **kwargs)	Method-chainable to_datetime.
jitter(df, column_name, dest_column_name, scale)	Adds Gaussian noise (jitter) to the values of a column.

Filtering

take_first(df, subset, by[, ascending])	Take the first row within each group specified by subset.
filter_string(df, column_name, search_string)	Filter a string-based column according to whether it contains a substring.
filter_on(df, criteria[, complement])	Return a dataframe filtered on a particular criteria.
filter_date(df, column_name[, start_date, …])	Filter a date-based column based on certain criteria.
filter_column_isin(df, column_name, iterable)	Filter a dataframe for values in a column that exist in another iterable.
select_columns(df, search_column_names[, invert])	Method-chainable selection of columns.
dropnotnull(df, column_name)	Drop rows that do not have null values in the given column.
get_dupes(df[, column_names])	Return all duplicate rows.

Preprocessing

bin_numeric(df, from_column_name, to_column_name)	Generate a new column that labels bins for a specified numeric column.
encode_categorical(df[, column_names])	Encode the specified columns with Pandas’ category dtype.
impute(df, column_name[, value, …])	Method-chainable imputation of values in a column.
label_encode(df, column_names)	Convert labels into numerical data.
min_max_scale(df[, old_min, old_max, …])	Scales data to between a minimum and maximum value.
get_features_targets(*args, **kwargs)

Other

then(df, func)	Add an arbitrary function to run in the pyjanitor method chain.
also(df, func, *args, **kwargs)	Add an arbitrary function with no return value to run in the pyjanitor method chain.
shuffle(df[, random_state, reset_index])	Shuffle the rows of the DataFrame.
count_cumulative_unique(df, column_name, …)	Generates a running total of cumulative unique values in a given column.
sort_naturally(df, column_name, …)	Sort a DataFrame by a column using “natural” sorting.
expand_grid([df, df_key, others])	Creates a dataframe from a cartesian combination of all inputs.
flag_nulls(df[, column_name, columns])	Creates a new column to indicate whether you have null values in a given row.
move(df, source, target[, position, axis])	Move column or row to a position adjacent to another column or row in dataframe.
toset(series)	Return a set of the values.
unionize_dataframe_categories(*dataframes[, …])	Given a group of dataframes which contain some categorical columns, for each categorical column present, find all the possible categories across all the dataframes which have that column.
groupby_topk(df, groupby_column_name, …[, …])	Return top k rows from a groupby of a set of columns.
complete(df[, columns, fill_value, by])	This function turns implicit missing values into explicit missing values.
pivot_longer(df[, index, column_names, …])	Unpivots a DataFrame from ‘wide’ to ‘long’ format.
pivot_wider(df[, index, names_from, …])	Reshapes data from long to wide form.