DataFrame

Constructor

DataFrame([data, index, columns, dtype, ...])

Attributes and underlying data

Axes

DataFrame.index
DataFrame.columns

DataFrame.dtypes	Return the dtypes in the DataFrame.
DataFrame.memory_usage([index, deep])	Return the memory usage of each column in bytes.
DataFrame.ndim	Return an int representing the number of axes / array dimensions.
DataFrame.select_dtypes([include, exclude])	Return a subset of the DataFrame's columns based on the column dtypes.
DataFrame.shape

Conversion

DataFrame.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
DataFrame.convert_dtypes([infer_objects, ...])	Convert columns to best possible dtypes using dtypes supporting pd.NA.
DataFrame.copy()
DataFrame.infer_objects([copy])	Attempt to infer better dtypes for object columns.

Indexing, iteration

DataFrame.at	Access a single value for a row/column label pair.
DataFrame.head([n])	Return the first n rows.
DataFrame.iat	Access a single value for a row/column pair by integer position.
DataFrame.iloc	Purely integer-location based indexing for selection by position.
DataFrame.insert(loc, column, value[, ...])	Insert column into DataFrame at specified location.
DataFrame.loc	Access a group of rows and columns by label(s) or a boolean array.
DataFrame.mask(cond[, other, inplace, axis, ...])	Replace values where the condition is True.
DataFrame.pop(item)	Return item and drop from frame.
DataFrame.query(expr[, inplace])	Query the columns of a DataFrame with a boolean expression.
DataFrame.tail([n])	Return the last n rows.
DataFrame.xs(key[, axis, level, drop_level])	Return cross-section from the Series/DataFrame.
DataFrame.where(cond[, other, inplace, ...])	Replace values where the condition is False.

Binary operator functions

DataFrame.add(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator add).
DataFrame.sub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator subtract).
DataFrame.mul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator mul).
DataFrame.div(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
DataFrame.truediv(other[, axis, level, ...])	Get Floating division of dataframe and other, element-wise (binary operator truediv).
DataFrame.floordiv(other[, axis, level, ...])	Get Integer division of dataframe and other, element-wise (binary operator floordiv).
DataFrame.mod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator mod).
DataFrame.pow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator pow).
DataFrame.dot(other)	Compute the matrix multiplication between the DataFrame and other.
DataFrame.radd(other[, axis, level, fill_value])	Get Addition of dataframe and other, element-wise (binary operator radd).
DataFrame.rsub(other[, axis, level, fill_value])	Get Subtraction of dataframe and other, element-wise (binary operator rsubtract).
DataFrame.rmul(other[, axis, level, fill_value])	Get Multiplication of dataframe and other, element-wise (binary operator rmul).
DataFrame.rdiv(other[, axis, level, fill_value])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
DataFrame.rtruediv(other[, axis, level, ...])	Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
DataFrame.rfloordiv(other[, axis, level, ...])	Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
DataFrame.rmod(other[, axis, level, fill_value])	Get Modulo of dataframe and other, element-wise (binary operator rmod).
DataFrame.rpow(other[, axis, level, fill_value])	Get Exponential power of dataframe and other, element-wise (binary operator rpow).
DataFrame.lt(other[, axis, level, fill_value])	Get Less than of dataframe and other, element-wise (binary operator lt).
DataFrame.gt(other[, axis, level, fill_value])	Get Greater than of dataframe and other, element-wise (binary operator gt).
DataFrame.le(other[, axis, level, fill_value])	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
DataFrame.ge(other[, axis, level, fill_value])	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
DataFrame.ne(other[, axis, level, fill_value])	Get Not equal to of dataframe and other, element-wise (binary operator ne).
DataFrame.eq(other[, axis, level, fill_value])	Get Equal to of dataframe and other, element-wise (binary operator eq).
DataFrame.combine(other, func[, fill_value, ...])	Perform column-wise combine with another DataFrame.
DataFrame.combine_first(other)	Update null elements with value in the same location in other.

Function application, GroupBy & window

DataFrame.apply(func[, axis, raw, ...])	Apply a function along an axis of the DataFrame.
DataFrame.applymap(func[, na_action, ...])	Apply a function to a Dataframe elementwise.
DataFrame.agg([func, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.aggregate([func, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.ewm([com, span, halflife, alpha, ...])	Provide exponential weighted functions.
DataFrame.expanding([min_periods, shift, ...])	Provide expanding transformations.
DataFrame.groupby([by, level, as_index, ...])	Group DataFrame using a mapper or by a Series of columns.
DataFrame.map(func[, na_action, dtypes, ...])	Apply a function to a Dataframe elementwise.
DataFrame.rolling(window[, min_periods, ...])	Provide rolling window calculations.
DataFrame.transform(func[, axis, dtypes, ...])	Call func on self producing a DataFrame with transformed values.

Computations / descriptive stats

DataFrame.abs()
DataFrame.all([axis, bool_only, skipna, ...])
DataFrame.any([axis, bool_only, skipna, ...])
DataFrame.clip([lower, upper, axis, inplace])	Trim values at input threshold(s).
DataFrame.count([axis, level, numeric_only])
DataFrame.corr([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values.
DataFrame.corrwith(other[, axis, drop, method])	Compute pairwise correlation.
DataFrame.cov([min_periods, ddof, numeric_only])	Compute pairwise covariance of columns, excluding NA/null values.
DataFrame.describe([percentiles, include, ...])	Generate descriptive statistics.
DataFrame.diff([periods, axis])	First discrete difference of element.
DataFrame.eval(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
DataFrame.max([axis, skipna, level, ...])
DataFrame.mean([axis, skipna, level, ...])
DataFrame.median([axis, skipna, level, ...])
DataFrame.min([axis, skipna, level, ...])
DataFrame.mode([axis, numeric_only, dropna, ...])	Get the mode(s) of each element along the selected axis.
DataFrame.nunique([axis, dropna])	Count distinct observations over requested axis.
DataFrame.pct_change([periods, fill_method, ...])	Percentage change between the current and a prior element.
DataFrame.prod([axis, skipna, level, ...])
DataFrame.product([axis, skipna, level, ...])
DataFrame.quantile([q, axis, numeric_only, ...])	Return values at the given quantile over requested axis.
DataFrame.rank([axis, method, numeric_only, ...])	Compute numerical data ranks (1 through n) along axis.
DataFrame.round([decimals])	Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, ...])
DataFrame.std([axis, skipna, level, ddof, ...])
DataFrame.sum([axis, skipna, level, ...])
DataFrame.value_counts([subset, normalize, ...])
DataFrame.var([axis, skipna, level, ddof, ...])

Reindexing / selection / label manipulation

DataFrame.add_prefix(prefix)	Prefix labels with string prefix.
DataFrame.add_suffix(suffix)	Suffix labels with string suffix.
DataFrame.align(other[, join, axis, level, ...])	Align two objects on their axes with the specified join method.
DataFrame.at_time(time[, axis])	Select values at particular time of day (e.g., 9:30AM).
DataFrame.between_time(start_time, end_time)	Select values between particular times of the day (e.g., 9:00-9:30 AM).
DataFrame.drop([labels, axis, index, ...])	Drop specified labels from rows or columns.
DataFrame.drop_duplicates([subset, keep, ...])	Return DataFrame with duplicate rows removed.
DataFrame.droplevel(level[, axis])	Return Series/DataFrame with requested index / column level(s) removed.
DataFrame.duplicated([subset, keep, method])	Return boolean Series denoting duplicate rows.
DataFrame.filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
DataFrame.head([n])	Return the first n rows.
DataFrame.idxmax([axis, skipna])	Return index of first occurrence of maximum over requested axis.
DataFrame.idxmin([axis, skipna])	Return index of first occurrence of minimum over requested axis.
DataFrame.reindex([labels, index, columns, ...])	Conform Series/DataFrame to new index with optional filling logic.
DataFrame.reindex_like(other[, method, ...])	Return an object with matching indices as other object.
DataFrame.rename([mapper, index, columns, ...])	Alter axes labels.
DataFrame.rename_axis([mapper, index, ...])	Set the name of the axis for the index or columns.
DataFrame.reset_index([level, drop, ...])	Reset the index, or a level of it.
DataFrame.sample([n, frac, replace, ...])	Return a random sample of items from an axis of object.
DataFrame.set_axis(labels[, axis, inplace])	Assign desired index to given axis.
DataFrame.set_index(keys[, drop, append, ...])	Set the DataFrame index using existing columns.
DataFrame.take(indices[, axis])	Return the elements in the given positional indices along an axis.
DataFrame.truncate([before, after, axis, copy])	Truncate a Series or DataFrame before and after some index value.

Missing data handling

DataFrame.dropna([axis, how, thresh, ...])	Remove missing values.
DataFrame.fillna([value, method, axis, ...])	Fill NA/NaN values using the specified method.
DataFrame.isna()	Detect missing values.
DataFrame.isnull()	Detect missing values.
DataFrame.notna()	Detect existing (non-missing) values.
DataFrame.notnull()	Detect existing (non-missing) values.

Reshaping, sorting, transposing

DataFrame.melt([id_vars, value_vars, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
DataFrame.nlargest(n, columns[, keep])	Return the first n rows ordered by columns in descending order.
DataFrame.nsmallest(n, columns[, keep])	Return the first n rows ordered by columns in ascending order.
DataFrame.pivot(columns[, index, values])	Return reshaped DataFrame organized by given index / column values.
DataFrame.pivot_table([values, index, ...])	Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.reorder_levels(order[, axis])	Rearrange index levels using input order.
DataFrame.sort_values(by[, axis, ascending, ...])	Sort by the values along either axis.
DataFrame.sort_index([axis, level, ...])	Sort object by labels (along an axis).
DataFrame.swaplevel([i, j, axis])	Swap levels i and j in a MultiIndex.
DataFrame.stack([level, dropna])	Stack the prescribed level(s) from columns to index.
DataFrame.unstack([level, fill_value])	Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

Combining / comparing / joining / merging

DataFrame.append(other[, ignore_index, ...])	Append rows of other to the end of caller, returning a new object.
DataFrame.assign(**kwargs)	Assign new columns to a DataFrame.
DataFrame.compare(other[, align_axis, ...])	Compare to another DataFrame and show the differences.
DataFrame.join(other[, on, how, lsuffix, ...])	Join columns of another DataFrame.
DataFrame.merge(right[, how, on, left_on, ...])	Merge DataFrame or named Series objects with a database-style join.
DataFrame.update(other[, join, overwrite, ...])	Modify in place using non-NA values from another DataFrame.

Time series-related

DataFrame.first_valid_index()	Return index for first non-NA value or None, if no non-NA value is found.
DataFrame.last_valid_index()	Return index for last non-NA value or None, if no non-NA value is found.
DataFrame.shift([periods, freq, axis, ...])	Shift index by desired number of periods with an optional time freq.
DataFrame.tshift([periods, freq, axis])	Shift the time index, using the index's frequency if available.

Plotting

DataFrame.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot.<kind>.

DataFrame.plot

DataFrame.plot.area(*args, **kwargs)	Draw a stacked area plot.
DataFrame.plot.bar(*args, **kwargs)	Vertical bar plot.
DataFrame.plot.barh(*args, **kwargs)	Make a horizontal bar plot.
DataFrame.plot.box(*args, **kwargs)	Make a box plot of the DataFrame columns.
DataFrame.plot.density(*args, **kwargs)	Generate Kernel Density Estimate plot using Gaussian kernels.
DataFrame.plot.hexbin(*args, **kwargs)	Generate a hexagonal binning plot.
DataFrame.plot.hist(*args, **kwargs)	Draw one histogram of the DataFrame's columns.
DataFrame.plot.kde(*args, **kwargs)	Generate Kernel Density Estimate plot using Gaussian kernels.
DataFrame.plot.line(*args, **kwargs)	Plot Series or DataFrame as lines.
DataFrame.plot.pie(*args, **kwargs)	Generate a pie plot.
DataFrame.plot.scatter(*args, **kwargs)	Create a scatter plot with varying marker point size and color.

Serialization / IO / conversion

DataFrame.from_dict(data[, orient, dtype, ...])	Construct DataFrame from dict of array-like or dicts.
DataFrame.from_records(data[, index, ...])	Convert structured or record ndarray to DataFrame.
DataFrame.to_clipboard(*[, excel, sep, ...])	Copy object to the system clipboard.
DataFrame.to_csv(path[, sep, na_rep, ...])	Write object to a comma-separated values (csv) file.
DataFrame.to_dict([orient, into, index, ...])	Convert the DataFrame to a dictionary.
DataFrame.to_json([path, orient, ...])	Convert the object to a JSON string.
DataFrame.to_odps_table(table[, partition, ...])	Write DataFrame object into a MaxCompute (ODPS) table.
DataFrame.to_pandas([session])
DataFrame.to_parquet(path[, engine, ...])	Write a DataFrame to the binary parquet format, each chunk will be written to a Parquet file.

MaxFrame Extensions

DataFrame.mf.apply_chunk(func[, batch_rows, ...])	Apply a function that takes pandas DataFrame and outputs pandas DataFrame/Series.
DataFrame.mf.collect_kv([columns, kv_delim, ...])	Merge values in specified columns into a key-value represented column.
DataFrame.mf.extract_kv([columns, kv_delim, ...])	Extract values in key-value represented columns into standalone columns.
DataFrame.mf.flatmap(func[, dtypes, raw, args])	Apply the given function to each row and then flatten results.
DataFrame.mf.map_reduce([mapper, reducer, ...])	Map-reduce API over certain DataFrames.
DataFrame.mf.rebalance([axis, factor, ...])	Make data more balanced across entire cluster.
DataFrame.mf.reshuffle([group_by, sort_by, ...])	Shuffle data in DataFrame or Series to make data distribution more randomized.
DataFrame.rechunk(chunk_size[, reassign_worker])	Rechunk DataFrame, Series or Index data.

DataFrame.mf provides methods unique to MaxFrame. These methods are collated from application scenarios in MaxCompute and these can be accessed like DataFrame.mf.<function/property>.