DataFrame¶

Constructor¶

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

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Attributes and underlying data¶

Axes

`DataFrame.index`	The index (row labels) of the DataFrame.
`DataFrame.columns`	The column labels of the DataFrame.

`DataFrame.dtypes`	Return the dtypes in the DataFrame.
`DataFrame.select_dtypes`(self[, include, exclude])	Return a subset of the DataFrame’s columns based on the column dtypes.
`DataFrame.values`	Return a Numpy representation of the DataFrame.
`DataFrame.axes`	Return a list representing the axes of the DataFrame.
`DataFrame.ndim`	Return an int representing the number of axes / array dimensions.
`DataFrame.size`	Return an int representing the number of elements in this object.
`DataFrame.shape`	Return a tuple representing the dimensionality of the DataFrame.
`DataFrame.memory_usage`(self[, index, deep])	Return the memory usage of each column in bytes.
`DataFrame.empty`	Indicator whether DataFrame is empty.

Conversion¶

`DataFrame.astype`(self, dtype, copy, errors)	Cast a pandas object to a specified dtype `dtype`.
`DataFrame.infer_objects`(self)	Attempt to infer better dtypes for object columns.
`DataFrame.copy`(self, deep)	Make a copy of this object’s indices and data.
`DataFrame.isna`(self)	Detect missing values.
`DataFrame.notna`(self)	Detect existing (non-missing) values.
`DataFrame.bool`(self)	Return the bool of a single element PandasObject.

Indexing, iteration¶

`DataFrame.head`(self, n)	Return the first n rows.
`DataFrame.at`	Access a single value for a row/column label pair.
`DataFrame.iat`	Access a single value for a row/column pair by integer position.
`DataFrame.loc`	Access a group of rows and columns by label(s) or a boolean array.
`DataFrame.iloc`	Purely integer-location based indexing for selection by position.
`DataFrame.insert`(self, loc, column, value[, …])	Insert column into DataFrame at specified location.
`DataFrame.__iter__`(self)	Iterate over info axis.
`DataFrame.items`(self)	Iterate over (column name, Series) pairs.
`DataFrame.iteritems`(self)	Iterate over (column name, Series) pairs.
`DataFrame.keys`(self)	Get the ‘info axis’ (see Indexing for more).
`DataFrame.iterrows`(self)	Iterate over DataFrame rows as (index, Series) pairs.
`DataFrame.itertuples`(self[, index, name])	Iterate over DataFrame rows as namedtuples.
`DataFrame.lookup`(self, row_labels, col_labels)	Label-based “fancy indexing” function for DataFrame.
`DataFrame.pop`(self, item)	Return item and drop from frame.
`DataFrame.tail`(self, n)	Return the last n rows.
`DataFrame.xs`(self, key[, axis, level])	Return cross-section from the Series/DataFrame.
`DataFrame.get`(self, key[, default])	Get item from object for given key (ex: DataFrame column).
`DataFrame.isin`(self, values)	Whether each element in the DataFrame is contained in values.
`DataFrame.where`(self, cond[, other, …])	Replace values where the condition is False.
`DataFrame.mask`(self, cond[, other, inplace, …])	Replace values where the condition is True.
`DataFrame.query`(self, expr[, inplace])	Query the columns of a DataFrame with a boolean expression.

For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.

Binary operator functions¶

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

Function application, GroupBy & window¶

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

Computations / descriptive stats¶

`DataFrame.abs`(self)	Return a Series/DataFrame with absolute numeric value of each element.
`DataFrame.all`(self[, axis, bool_only, …])	Return whether all elements are True, potentially over an axis.
`DataFrame.any`(self[, axis, bool_only, …])	Return whether any element is True, potentially over an axis.
`DataFrame.clip`(self[, lower, upper, axis])	Trim values at input threshold(s).
`DataFrame.corr`(self[, method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values.
`DataFrame.corrwith`(self, other[, axis, …])	Compute pairwise correlation.
`DataFrame.count`(self[, axis, level, …])	Count non-NA cells for each column or row.
`DataFrame.cov`(self[, min_periods])	Compute pairwise covariance of columns, excluding NA/null values.
`DataFrame.cummax`(self[, axis, skipna])	Return cumulative maximum over a DataFrame or Series axis.
`DataFrame.cummin`(self[, axis, skipna])	Return cumulative minimum over a DataFrame or Series axis.
`DataFrame.cumprod`(self[, axis, skipna])	Return cumulative product over a DataFrame or Series axis.
`DataFrame.cumsum`(self[, axis, skipna])	Return cumulative sum over a DataFrame or Series axis.
`DataFrame.describe`(self[, percentiles, …])	Generate descriptive statistics.
`DataFrame.diff`(self[, periods, axis])	First discrete difference of element.
`DataFrame.eval`(self, expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
`DataFrame.kurt`(self[, axis, skipna, level, …])	Return unbiased kurtosis over requested axis.
`DataFrame.kurtosis`(self[, axis, skipna, …])	Return unbiased kurtosis over requested axis.
`DataFrame.mad`(self[, axis, skipna, level])	Return the mean absolute deviation of the values for the requested axis.
`DataFrame.max`(self[, axis, skipna, level, …])	Return the maximum of the values for the requested axis.
`DataFrame.mean`(self[, axis, skipna, level, …])	Return the mean of the values for the requested axis.
`DataFrame.median`(self[, axis, skipna, …])	Return the median of the values for the requested axis.
`DataFrame.min`(self[, axis, skipna, level, …])	Return the minimum of the values for the requested axis.
`DataFrame.mode`(self[, axis, numeric_only, …])	Get the mode(s) of each element along the selected axis.
`DataFrame.pct_change`(self[, periods, …])	Percentage change between the current and a prior element.
`DataFrame.prod`(self[, axis, skipna, level, …])	Return the product of the values for the requested axis.
`DataFrame.product`(self[, axis, skipna, …])	Return the product of the values for the requested axis.
`DataFrame.quantile`(self[, q, axis, …])	Return values at the given quantile over requested axis.
`DataFrame.rank`(self[, axis])	Compute numerical data ranks (1 through n) along axis.
`DataFrame.round`(self[, decimals])	Round a DataFrame to a variable number of decimal places.
`DataFrame.sem`(self[, axis, skipna, level, …])	Return unbiased standard error of the mean over requested axis.
`DataFrame.skew`(self[, axis, skipna, level, …])	Return unbiased skew over requested axis.
`DataFrame.sum`(self[, axis, skipna, level, …])	Return the sum of the values for the requested axis.
`DataFrame.std`(self[, axis, skipna, level, …])	Return sample standard deviation over requested axis.
`DataFrame.var`(self[, axis, skipna, level, …])	Return unbiased variance over requested axis.
`DataFrame.nunique`(self[, axis, dropna])	Count distinct observations over requested axis.

Reindexing / selection / label manipulation¶

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

Missing data handling¶

`DataFrame.dropna`(self[, axis, how, thresh, …])	Remove missing values.
`DataFrame.fillna`(self[, value, method, …])	Fill NA/NaN values using the specified method.
`DataFrame.replace`(self[, to_replace, value, …])	Replace values given in to_replace with value.
`DataFrame.interpolate`(self[, method, axis, …])	Interpolate values according to different methods.

Reshaping, sorting, transposing¶

`DataFrame.droplevel`(self, level[, axis])	Return DataFrame with requested index / column level(s) removed.
`DataFrame.pivot`(self[, index, columns, values])	Return reshaped DataFrame organized by given index / column values.
`DataFrame.pivot_table`(self[, values, index, …])	Create a spreadsheet-style pivot table as a DataFrame.
`DataFrame.reorder_levels`(self, order[, axis])	Rearrange index levels using input order.
`DataFrame.sort_values`(self, by[, axis, …])	Sort by the values along either axis.
`DataFrame.sort_index`(self[, axis, level])	Sort object by labels (along an axis).
`DataFrame.nlargest`(self, n, columns[, keep])	Return the first n rows ordered by columns in descending order.
`DataFrame.nsmallest`(self, n, columns[, keep])	Return the first n rows ordered by columns in ascending order.
`DataFrame.swaplevel`(self[, i, j, axis])	Swap levels i and j in a MultiIndex on a particular axis.
`DataFrame.stack`(self[, level, dropna])	Stack the prescribed level(s) from columns to index.
`DataFrame.unstack`(self[, level, fill_value])	Pivot a level of the (necessarily hierarchical) index labels.
`DataFrame.swapaxes`(self, axis1, axis2[, copy])	Interchange axes and swap values axes appropriately.
`DataFrame.melt`(self[, id_vars, value_vars, …])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
`DataFrame.explode`(self, column, Tuple])	Transform each element of a list-like to a row, replicating index values.
`DataFrame.squeeze`(self[, axis])	Squeeze 1 dimensional axis objects into scalars.
`DataFrame.to_xarray`(self)	Return an xarray object from the pandas object.
`DataFrame.T`	Transpose index and columns.
`DataFrame.transpose`(self, \*args, copy)	Transpose index and columns.

Combining / joining / merging¶

`DataFrame.append`(self, other[, …])	Append rows of other to the end of caller, returning a new object.
`DataFrame.assign`(self, \\kwargs)	Assign new columns to a DataFrame.
`DataFrame.join`(self, other[, on, how, …])	Join columns of another DataFrame.
`DataFrame.merge`(self, right[, how, on, …])	Merge DataFrame or named Series objects with a database-style join.
`DataFrame.update`(self, other[, join, …])	Modify in place using non-NA values from another DataFrame.

Metadata¶

DataFrame.attrs is a dictionary for storing global metadata for this DataFrame.

Warning

DataFrame.attrs is considered experimental and may change without warning.

DataFrame.attrs

Dictionary of global attributes on this object.

Plotting¶

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

DataFrame.plot([x, y, kind, ax, ….])

DataFrame plotting accessor and method

`DataFrame.plot.area`(self[, x, y])	Draw a stacked area plot.
`DataFrame.plot.bar`(self[, x, y])	Vertical bar plot.
`DataFrame.plot.barh`(self[, x, y])	Make a horizontal bar plot.
`DataFrame.plot.box`(self[, by])	Make a box plot of the DataFrame columns.
`DataFrame.plot.density`(self[, bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
`DataFrame.plot.hexbin`(self, x, y[, C, …])	Generate a hexagonal binning plot.
`DataFrame.plot.hist`(self[, by, bins])	Draw one histogram of the DataFrame’s columns.
`DataFrame.plot.kde`(self[, bw_method, ind])	Generate Kernel Density Estimate plot using Gaussian kernels.
`DataFrame.plot.line`(self[, x, y])	Plot Series or DataFrame as lines.
`DataFrame.plot.pie`(self, \\kwargs)	Generate a pie plot.
`DataFrame.plot.scatter`(self, x, y[, s, c])	Create a scatter plot with varying marker point size and color.

`DataFrame.boxplot`(self[, column, by, ax, …])	Make a box plot from DataFrame columns.
`DataFrame.hist`(data[, column, by, grid, …])	Make a histogram of the DataFrame’s.

Sparse accessor¶

Sparse-dtype specific methods and attributes are provided under the DataFrame.sparse accessor.

DataFrame.sparse.density

Ratio of non-sparse points to total (dense) data points.

`DataFrame.sparse.from_spmatrix`(data[, …])	Create a new DataFrame from a scipy sparse matrix.
`DataFrame.sparse.to_coo`(self)	Return the contents of the frame as a sparse SciPy COO matrix.
`DataFrame.sparse.to_dense`(self)	Convert a DataFrame with sparse values to dense.

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.info`(self[, verbose, buf, …])	Print a concise summary of a DataFrame.
`DataFrame.to_parquet`(self, path[, engine, …])	Write a DataFrame to the binary parquet format.
`DataFrame.to_pickle`(self, path, compression, …)	Pickle (serialize) object to file.
`DataFrame.to_csv`(self, path_or_buf, …)	Write object to a comma-separated values (csv) file.
`DataFrame.to_hdf`(self, path_or_buf, key, …)	Write the contained data to an HDF5 file using HDFStore.
`DataFrame.to_sql`(self, name, con[, schema, …])	Write records stored in a DataFrame to a SQL database.
`DataFrame.to_dict`(self[, orient, into])	Convert the DataFrame to a dictionary.
`DataFrame.to_excel`(self, excel_writer[, …])	Write object to an Excel sheet.
`DataFrame.to_json`(self, path_or_buf, …)	Convert the object to a JSON string.
`DataFrame.to_html`(self[, buf, columns, …])	Render a DataFrame as an HTML table.
`DataFrame.to_feather`(self, path)	Write out the binary feather-format for DataFrames.
`DataFrame.to_latex`(self[, buf, columns, …])	Render object to a LaTeX tabular, longtable, or nested table/tabular.
`DataFrame.to_stata`(self, path[, …])	Export DataFrame object to Stata dta format.
`DataFrame.to_gbq`(self, destination_table[, …])	Write a DataFrame to a Google BigQuery table.
`DataFrame.to_records`(self[, index, …])	Convert DataFrame to a NumPy record array.
`DataFrame.to_string`(self, buf, pathlib.Path, …)	Render a DataFrame to a console-friendly tabular output.
`DataFrame.to_clipboard`(self, excel, sep, …)	Copy object to the system clipboard.
`DataFrame.to_markdown`(self, buf, …)	Print DataFrame in Markdown-friendly format.
`DataFrame.style`	Returns a Styler object.

`DataFrame.asfreq`(self, freq[, method, …])	Convert TimeSeries to specified frequency.
`DataFrame.asof`(self, where[, subset])	Return the last row(s) without any NaNs before where.
`DataFrame.shift`(self[, periods, freq, axis, …])	Shift index by desired number of periods with an optional time freq.
`DataFrame.slice_shift`(self, periods[, axis])	Equivalent to shift without copying data.
`DataFrame.tshift`(self, periods[, freq, axis])	Shift the time index, using the index’s frequency if available.
`DataFrame.first_valid_index`(self)	Return index for first non-NA/null value.
`DataFrame.last_valid_index`(self)	Return index for last non-NA/null value.
`DataFrame.resample`(self, rule[, axis, …])	Resample time-series data.
`DataFrame.to_period`(self[, freq, axis, copy])	Convert DataFrame from DatetimeIndex to PeriodIndex.
`DataFrame.to_timestamp`(self[, freq, how, …])	Cast to DatetimeIndex of timestamps, at beginning of period.
`DataFrame.tz_convert`(self, tz[, axis, level])	Convert tz-aware axis to target time zone.
`DataFrame.tz_localize`(self, tz[, axis, …])	Localize tz-naive index of a Series or DataFrame to target time zone.