DataFrame¶

Constructor¶

DataFrame([data, columns, orient])

A DataFrame is a two-dimensional data structure that represents data as a table with rows and columns.

Attributes¶

`DataFrame.shape`	Get the shape of the DataFrame.
`DataFrame.height`	Get the height of the DataFrame.
`DataFrame.width`	Get the width of the DataFrame.
`DataFrame.columns`	Get or set column names.
`DataFrame.dtypes`	Get dtypes of columns in DataFrame.
`DataFrame.schema`	Get a dict[column name, DataType]

Conversion¶

`DataFrame.to_arrow`()	Collect the underlying arrow arrays in an Arrow Table.
`DataFrame.to_avro`(file[, compression])	Deprecated since version 0.13.12.
`DataFrame.to_json`()	Deprecated since version 0.13.12.
`DataFrame.to_pandas`(*args[, date_as_object])	Cast to a pandas DataFrame.
`DataFrame.to_csv`([file, has_header, sep])	Deprecated since version 0.13.12.
`DataFrame.to_ipc`(file[, compression])	Deprecated since version 0.13.12.
`DataFrame.to_parquet`(file[, compression, ...])	Deprecated since version 0.13.12.
`DataFrame.to_numpy`()	Convert DataFrame to a 2d numpy array.
`DataFrame.to_dict`()	Convert DataFrame to a dictionary mapping column name to values.
`DataFrame.to_dicts`()	Convert every row to a dictionary.
`DataFrame.to_struct`(name)	Convert a `DataFrame` to a `Series` of type `Struct`

Aggregation¶

`DataFrame.max`()	Aggregate the columns of this DataFrame to their maximum value.
`DataFrame.min`()	Aggregate the columns of this DataFrame to their minimum value.
`DataFrame.sum`()	Aggregate the columns of this DataFrame to their sum value.
`DataFrame.mean`()	Aggregate the columns of this DataFrame to their mean value.
`DataFrame.std`()	Aggregate the columns of this DataFrame to their standard deviation value.
`DataFrame.var`()	Aggregate the columns of this DataFrame to their variance value.
`DataFrame.median`()	Aggregate the columns of this DataFrame to their median value.
`DataFrame.quantile`(quantile[, interpolation])	Aggregate the columns of this DataFrame to their quantile value.
`DataFrame.product`()	Aggregate the columns of this DataFrame to their product values

Descriptive stats¶

`DataFrame.describe`()	Summary statistics for a DataFrame.
`DataFrame.estimated_size`()	Returns an estimation of the total (heap) allocated size of the DataFrame in bytes.
`DataFrame.is_duplicated`()	Get a mask of all duplicated rows in this DataFrame.
`DataFrame.is_unique`()	Get a mask of all unique rows in this DataFrame.
`DataFrame.n_chunks`()	Get number of chunks used by the ChunkedArrays of this DataFrame.
`DataFrame.null_count`()	Create a new DataFrame that shows the null counts per column.
`DataFrame.is_empty`()	Check if the dataframe is empty

Computations¶

`DataFrame.hash_rows`([k0, k1, k2, k3])	Hash and combine the rows in this DataFrame.
`DataFrame.fold`(operation)	Apply a horizontal reduction on a DataFrame.

Manipulation/ selection¶

`DataFrame.rename`(mapping)	Rename column names.
`DataFrame.with_row_count`([name, offset])	Add a column at index 0 that counts the rows.
`DataFrame.insert_at_idx`(index, series)	Insert a Series at a certain column index.
`DataFrame.filter`(predicate)	Filter the rows in the DataFrame based on a predicate expression.
`DataFrame.find_idx_by_name`(name)	Find the index of a column by name.
`DataFrame.select_at_idx`(idx)	Select column at index location.
`DataFrame.replace_at_idx`(index, series)	Replace a column at an index location.
`DataFrame.sort`()	Sort the DataFrame by column.
`DataFrame.replace`(column, new_col)	Replace a column by a new Series.
`DataFrame.slice`(offset, length)	Slice this DataFrame over the rows direction.
`DataFrame.limit`([length])	Get first N rows as DataFrame.
`DataFrame.head`([length])	Get first N rows as DataFrame.
`DataFrame.tail`([length])	Get last N rows as DataFrame.
`DataFrame.drop_nulls`([subset])	Return a new DataFrame where the null values are dropped.
`DataFrame.drop`(name)	Remove column from DataFrame and return as new.
`DataFrame.drop_in_place`(name)	Drop in place.
`DataFrame.to_series`([index])	Select column as Series at index location.
`DataFrame.clone`()	Very cheap deep clone.
`DataFrame.get_columns`()	Get the DataFrame as a List of Series.
`DataFrame.get_column`(name)	Get a single column as Series by name.
`DataFrame.fill_null`(strategy)	Fill null values using a filling strategy, literal, or Expr.
`DataFrame.fill_nan`(fill_value)	Fill floating point NaN values by an Expression evaluation.
`DataFrame.explode`(columns)	Explode DataFrame to long format by exploding a column with Lists.
`DataFrame.pivot`(values, index, columns[, ...])	Create a spreadsheet-style pivot table as a DataFrame.
`DataFrame.melt`([id_vars, value_vars, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
`DataFrame.shift`(periods)	Shift the values by a given period and fill the parts that will be empty due to this operation with Nones.
`DataFrame.shift_and_fill`(periods, fill_value)	Shift the values by a given period and fill the parts that will be empty due to this operation with the result of the fill_value expression.
`DataFrame.with_column`(column)	Return a new DataFrame with the column added or replaced.
`DataFrame.hstack`(columns[, in_place])	Return a new DataFrame grown horizontally by stacking multiple Series to it.
`DataFrame.vstack`()	Grow this DataFrame vertically by stacking a DataFrame to it.
`DataFrame.extend`(other)	Extend the memory backed by this DataFrame with the values from other.
`DataFrame.groupby`(by[, maintain_order])	Start a groupby operation.
`DataFrame.groupby_dynamic`(index_column, every)	Groups based on a time value (or index value of type Int32, Int64).
`DataFrame.groupby_rolling`(index_column, period)	Create rolling groups based on a time column (or index value of type Int32, Int64).
`DataFrame.select`(exprs)	Select columns from this DataFrame.
`DataFrame.with_columns`(exprs)	Add or overwrite multiple columns in a DataFrame.
`DataFrame.sample`([n, frac, ...])	Sample from this DataFrame by setting either n or frac.
`DataFrame.row`(index)	Get a row as tuple.
`DataFrame.rows`()	Convert columnar data to rows as python tuples.
`DataFrame.to_dummies`()	Get one hot encoded dummy variables.
`DataFrame.distinct`([maintain_order, subset, ...])	Deprecated since version 0.13.13.
`DataFrame.unique`([maintain_order, subset, keep])	Drop duplicate rows from this DataFrame.
`DataFrame.shrink_to_fit`()	Shrink memory usage of this DataFrame to fit the exact capacity needed to hold the data.
`DataFrame.rechunk`()	Rechunk the data in this DataFrame to a contiguous allocation.
`DataFrame.pipe`(func, args, *kwargs)	Apply a function on Self.
`DataFrame.join`(df[, left_on, right_on, on, ...])	SQL like joins.
`DataFrame.join_asof`(df[, left_on, right_on, ...])	Perform an asof join.
`DataFrame.interpolate`()	Interpolate intermediate values.
`DataFrame.transpose`([include_header, ...])	Transpose a DataFrame over the diagonal.
`DataFrame.partition_by`()	Split into multiple DataFrames partitioned by groups.
`DataFrame.upsample`(time_column, every[, ...])	Upsample a DataFrame at a regular frequency.
`DataFrame.unnest`(names)	Decompose a struct into its fields.

Apply¶

DataFrame.apply(f[, return_dtype, ...])

Apply a custom function over the rows of the DataFrame.

Various¶

`DataFrame.frame_equal`(other[, null_equal])	Check if DataFrame is equal to other.
`DataFrame.lazy`()	Start a lazy query from this point.

GroupBy¶

This namespace comes available by calling DataFrame.groupby(..).

`GroupBy.agg`(column_to_agg)	Use multiple aggregations on columns.
`GroupBy.apply`(f)	Apply a function over the groups as a sub-DataFrame.
`GroupBy.head`([n])	Return first n rows of each group.
`GroupBy.tail`([n])	Return last n rows of each group.
`GroupBy.get_group`(group_value)	Select a single group as a new DataFrame.
`GroupBy.groups`()	Return a DataFrame with:
`GroupBy.pivot`(pivot_column, values_column)	Do a pivot operation based on the group key, a pivot column and an aggregation function on the values column.
`GroupBy.first`()	Aggregate the first values in the group.
`GroupBy.last`()	Aggregate the last values in the group.
`GroupBy.sum`()	Reduce the groups to the sum.
`GroupBy.min`()	Reduce the groups to the minimal value.
`GroupBy.max`()	Reduce the groups to the maximal value.
`GroupBy.count`()	Count the number of values in each group.
`GroupBy.mean`()	Reduce the groups to the mean values.
`GroupBy.n_unique`()	Count the unique values per group.
`GroupBy.quantile`(quantile[, interpolation])	Compute the quantile per group.
`GroupBy.median`()	Return the median per group.
`GroupBy.agg_list`()	Aggregate the groups into Series.

Pivot¶

This namespace comes available by calling DataFrame.groupby(..).pivot

Note that this API is deprecated in favor of `DataFrame.pivot`

`PivotOps.first`()	Get the first value per group.
`PivotOps.last`()	Get the last value per group.
`PivotOps.sum`()	Get the sum per group.
`PivotOps.min`()	Get the minimal value per group.
`PivotOps.max`()	Get the maximal value per group.
`PivotOps.mean`()	Get the mean value per group.
`PivotOps.count`()	Count the values per group.
`PivotOps.median`()	Get the median value per group.

polars.internals.series.StructNameSpace.rename_fields polars.DataFrame