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

Conversion

DataFrame.astype(dtype[, copy, errors])	Cast a pandas object to a specified dtype dtype.
DataFrame.copy()
DataFrame.isna()	Detect missing values.
DataFrame.notna()	Detect existing (non-missing) values.

Indexing, iteration

DataFrame.head([n])	Return the first n rows.
DataFrame.at	Access a single value for a row/column label pair.
DataFrame.iat
DataFrame.loc
DataFrame.iloc
DataFrame.insert(loc, column, value[, ...])	Insert column into DataFrame at specified location.
DataFrame.iterrows([batch_size, session])	Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.itertuples([index, name, ...])	Iterate over DataFrame rows as namedtuples.
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.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])	Get Less than of dataframe and other, element-wise (binary operator lt).
DataFrame.gt(other[, axis, level])	Get Greater than of dataframe and other, element-wise (binary operator gt).
DataFrame.le(other[, axis, level])	Get Less than or equal to of dataframe and other, element-wise (binary operator le).
DataFrame.ge(other[, axis, level])	Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
DataFrame.ne(other[, axis, level])	Get Not equal to of dataframe and other, element-wise (binary operator ne).
DataFrame.eq(other[, axis, level])	Get Equal to of dataframe and other, element-wise (binary operator eq).

Function application, GroupBy & window

DataFrame.apply(func[, axis, raw, ...])	Apply a function along an axis of the DataFrame.
DataFrame.agg([func, axis])
DataFrame.aggregate([func, axis])
DataFrame.transform(func[, axis, dtypes, ...])	Call func on self producing a DataFrame with transformed values.
DataFrame.groupby([by, level, as_index, ...])
DataFrame.rolling(window[, min_periods, ...])	Provide rolling window calculations.
DataFrame.expanding([min_periods, center, axis])	Provide expanding transformations.
DataFrame.ewm([com, span, halflife, alpha, ...])	Provide exponential weighted functions.

Computations / descriptive stats

DataFrame.abs()
DataFrame.all([axis, bool_only, skipna, ...])
DataFrame.any([axis, bool_only, skipna, ...])
DataFrame.corr([method, min_periods])	Compute pairwise correlation of columns, excluding NA/null values.
DataFrame.corrwith(other[, axis, drop, method])	Compute pairwise correlation.
DataFrame.count([axis, level, numeric_only, ...])
DataFrame.cummax([axis, skipna])
DataFrame.cummin([axis, skipna])
DataFrame.cumprod([axis, skipna])
DataFrame.cumsum([axis, skipna])
DataFrame.describe([percentiles, include, ...])
DataFrame.eval(expr[, inplace])	Evaluate a string describing operations on DataFrame columns.
DataFrame.kurt([axis, skipna, level, ...])
DataFrame.kurtosis([axis, skipna, level, ...])
DataFrame.max([axis, skipna, level, ...])
DataFrame.mean([axis, skipna, level, ...])
DataFrame.min([axis, skipna, level, ...])
DataFrame.nunique([axis, dropna, combine_size])	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.round([decimals])	Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, ...])
DataFrame.skew([axis, skipna, level, ...])
DataFrame.std([axis, skipna, level, ddof, ...])
DataFrame.sum([axis, skipna, level, ...])
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.drop([labels, axis, index, ...])	Drop specified labels from rows or columns.
DataFrame.drop_duplicates([subset, keep, ...])	Return DataFrame with duplicate rows removed.
DataFrame.duplicated([subset, keep, method])	Return boolean Series denoting duplicate rows.
DataFrame.head([n])	Return the first n rows.
DataFrame.reindex(*args, **kwargs)	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, ...])
DataFrame.tail([n])	Return the last n rows.

Missing data handling

DataFrame.backfill([axis, inplace, limit, ...])	Synonym for DataFrame.fillna() with method='bfill'.
DataFrame.bfill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='bfill'.
DataFrame.dropna([axis, how, thresh, ...])	Remove missing values.
DataFrame.ffill([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
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.
DataFrame.pad([axis, inplace, limit, downcast])	Synonym for DataFrame.fillna() with method='ffill'.
DataFrame.replace([to_replace, value, ...])	Replace values given in to_replace with value.

Reshaping, sorting, transposing

DataFrame.explode(column[, ignore_index])	Transform each element of a list-like to a row, replicating index values.
DataFrame.melt([id_vars, value_vars, ...])	Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
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.stack([level, dropna])	Stack the prescribed level(s) from columns to index.
DataFrame.T
DataFrame.transpose()	Transpose index and columns.

Combining / joining / merging

DataFrame.append(other[, ignore_index, ...])
DataFrame.assign(**kwargs)	Assign new columns to a DataFrame.
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.

Time series-related

DataFrame.diff([periods, axis])	First discrete difference of element.
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

alias of PlotAccessor

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.to_csv(path[, sep, na_rep, ...])	Write object to a comma-separated values (csv) file.
DataFrame.to_parquet(path[, engine, ...])	Write a DataFrame to the binary parquet format, each chunk will be written to a Parquet file.
DataFrame.to_sql(name, con[, schema, ...])	Write records stored in a DataFrame to a SQL database.

Misc

DataFrame.map_chunk(func[, args, kwargs, ...])	Apply function to each chunk.
DataFrame.rebalance([factor, axis, ...])	Make Data more balanced across entire cluster.