DataFrame([data, index, columns, dtype, …])
DataFrame
Axes
DataFrame.index
DataFrame.columns
DataFrame.dtypes
Return the dtypes in the DataFrame.
DataFrame.select_dtypes([include, exclude])
DataFrame.select_dtypes
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])
DataFrame.memory_usage
Return the memory usage of each column in bytes.
DataFrame.astype(dtype[, copy, errors])
DataFrame.astype
Cast a pandas object to a specified dtype dtype.
dtype
DataFrame.copy()
DataFrame.copy
DataFrame.isna()
DataFrame.isna
Detect missing values.
DataFrame.notna()
DataFrame.notna
Detect existing (non-missing) values.
DataFrame.head([n])
DataFrame.head
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[, …])
DataFrame.insert
Insert column into DataFrame at specified location.
DataFrame.iterrows([batch_size, session])
DataFrame.iterrows
Iterate over DataFrame rows as (index, Series) pairs.
DataFrame.itertuples([index, name, …])
DataFrame.itertuples
Iterate over DataFrame rows as namedtuples.
DataFrame.pop(item)
DataFrame.pop
Return item and drop from frame.
DataFrame.tail([n])
DataFrame.tail
Return the last n rows.
DataFrame.where(cond[, other, inplace, …])
DataFrame.where
Replace values where the condition is False.
DataFrame.mask(cond[, other, inplace, axis, …])
DataFrame.mask
Replace values where the condition is True.
DataFrame.add(other[, axis, level, fill_value])
DataFrame.add
Get Addition of dataframe and other, element-wise (binary operator add).
DataFrame.sub(other[, axis, level, fill_value])
DataFrame.sub
Get Subtraction of dataframe and other, element-wise (binary operator subtract).
DataFrame.mul(other[, axis, level, fill_value])
DataFrame.mul
Get Multiplication of dataframe and other, element-wise (binary operator mul).
DataFrame.div(other[, axis, level, fill_value])
DataFrame.div
Get Floating division of dataframe and other, element-wise (binary operator truediv).
DataFrame.truediv(other[, axis, level, …])
DataFrame.truediv
DataFrame.floordiv(other[, axis, level, …])
DataFrame.floordiv
Get Integer division of dataframe and other, element-wise (binary operator floordiv).
DataFrame.mod(other[, axis, level, fill_value])
DataFrame.mod
Get Modulo of dataframe and other, element-wise (binary operator mod).
DataFrame.pow(other[, axis, level, fill_value])
DataFrame.pow
Get Exponential power of dataframe and other, element-wise (binary operator pow).
DataFrame.dot(other)
DataFrame.dot
Compute the matrix multiplication between the DataFrame and other.
DataFrame.radd(other[, axis, level, fill_value])
DataFrame.radd
Get Addition of dataframe and other, element-wise (binary operator radd).
DataFrame.rsub(other[, axis, level, fill_value])
DataFrame.rsub
Get Subtraction of dataframe and other, element-wise (binary operator rsubtract).
DataFrame.rmul(other[, axis, level, fill_value])
DataFrame.rmul
Get Multiplication of dataframe and other, element-wise (binary operator rmul).
DataFrame.rdiv(other[, axis, level, fill_value])
DataFrame.rdiv
Get Floating division of dataframe and other, element-wise (binary operator rtruediv).
DataFrame.rtruediv(other[, axis, level, …])
DataFrame.rtruediv
DataFrame.rfloordiv(other[, axis, level, …])
DataFrame.rfloordiv
Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).
DataFrame.rmod(other[, axis, level, fill_value])
DataFrame.rmod
Get Modulo of dataframe and other, element-wise (binary operator rmod).
DataFrame.rpow(other[, axis, level, fill_value])
DataFrame.rpow
Get Exponential power of dataframe and other, element-wise (binary operator rpow).
DataFrame.lt(other[, axis, level])
DataFrame.lt
Get Less than of dataframe and other, element-wise (binary operator lt).
DataFrame.gt(other[, axis, level])
DataFrame.gt
Get Greater than of dataframe and other, element-wise (binary operator gt).
DataFrame.le(other[, axis, level])
DataFrame.le
Get Less than or equal to of dataframe and other, element-wise (binary operator le).
DataFrame.ge(other[, axis, level])
DataFrame.ge
Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).
DataFrame.ne(other[, axis, level])
DataFrame.ne
Get Not equal to of dataframe and other, element-wise (binary operator ne).
DataFrame.eq(other[, axis, level])
DataFrame.eq
Get Equal to of dataframe and other, element-wise (binary operator eq).
DataFrame.apply(func[, axis, raw, …])
DataFrame.apply
Apply a function along an axis of the DataFrame.
DataFrame.agg([func, axis])
DataFrame.agg
DataFrame.aggregate([func, axis])
DataFrame.aggregate
DataFrame.transform(func[, axis, dtypes])
DataFrame.transform
Call func on self producing a DataFrame with transformed values.
func
DataFrame.groupby([by, level, as_index, …])
DataFrame.groupby
DataFrame.rolling(window[, min_periods, …])
DataFrame.rolling
Provide rolling window calculations.
DataFrame.expanding([min_periods, center, axis])
DataFrame.expanding
Provide expanding transformations.
DataFrame.ewm([com, span, halflife, alpha, …])
DataFrame.ewm
Provide exponential weighted functions.
DataFrame.abs()
DataFrame.abs
DataFrame.all([axis, bool_only, skipna, …])
DataFrame.all
DataFrame.any([axis, bool_only, skipna, …])
DataFrame.any
DataFrame.corr([method, min_periods])
DataFrame.corr
Compute pairwise correlation of columns, excluding NA/null values.
DataFrame.corrwith(other[, axis, drop, method])
DataFrame.corrwith
Compute pairwise correlation.
DataFrame.count([axis, level, numeric_only, …])
DataFrame.count
DataFrame.cummax([axis, skipna])
DataFrame.cummax
DataFrame.cummin([axis, skipna])
DataFrame.cummin
DataFrame.cumprod([axis, skipna])
DataFrame.cumprod
DataFrame.cumsum([axis, skipna])
DataFrame.cumsum
DataFrame.describe([percentiles, include, …])
DataFrame.describe
DataFrame.kurt([axis, skipna, level, …])
DataFrame.kurt
DataFrame.kurtosis([axis, skipna, level, …])
DataFrame.kurtosis
DataFrame.max([axis, skipna, level, …])
DataFrame.max
DataFrame.mean([axis, skipna, level, …])
DataFrame.mean
DataFrame.min([axis, skipna, level, …])
DataFrame.min
DataFrame.nunique([axis, dropna, combine_size])
DataFrame.nunique
Count distinct observations over requested axis.
DataFrame.prod([axis, skipna, level, …])
DataFrame.prod
DataFrame.product([axis, skipna, level, …])
DataFrame.product
DataFrame.quantile([q, axis, numeric_only, …])
DataFrame.quantile
Return values at the given quantile over requested axis.
DataFrame.round([decimals])
DataFrame.round
Round a DataFrame to a variable number of decimal places.
DataFrame.sem([axis, skipna, level, ddof, …])
DataFrame.sem
DataFrame.skew([axis, skipna, level, …])
DataFrame.skew
DataFrame.std([axis, skipna, level, ddof, …])
DataFrame.std
DataFrame.sum([axis, skipna, level, …])
DataFrame.sum
DataFrame.var([axis, skipna, level, ddof, …])
DataFrame.var
DataFrame.drop([labels, axis, index, …])
DataFrame.drop
Drop specified labels from rows or columns.
DataFrame.drop_duplicates([subset, keep, …])
DataFrame.drop_duplicates
Return DataFrame with duplicate rows removed.
DataFrame.reindex(*args, **kwargs)
DataFrame.reindex
Conform Series/DataFrame to new index with optional filling logic.
DataFrame.rename([mapper, index, columns, …])
DataFrame.rename
Alter axes labels.
DataFrame.rename_axis([mapper, index, …])
DataFrame.rename_axis
Set the name of the axis for the index or columns.
DataFrame.reset_index([level, drop, …])
DataFrame.reset_index
Reset the index, or a level of it.
DataFrame.set_index(keys[, drop, append, …])
DataFrame.set_index
DataFrame.backfill([axis, inplace, limit, …])
DataFrame.backfill
Synonym for DataFrame.fillna() with method='bfill'.
DataFrame.fillna()
method='bfill'
DataFrame.bfill([axis, inplace, limit, downcast])
DataFrame.bfill
DataFrame.dropna([axis, how, thresh, …])
DataFrame.dropna
Remove missing values.
DataFrame.ffill([axis, inplace, limit, downcast])
DataFrame.ffill
Synonym for DataFrame.fillna() with method='ffill'.
method='ffill'
DataFrame.fillna([value, method, axis, …])
DataFrame.fillna
Fill NA/NaN values using the specified method.
DataFrame.isnull()
DataFrame.isnull
DataFrame.notnull()
DataFrame.notnull
DataFrame.pad([axis, inplace, limit, downcast])
DataFrame.pad
DataFrame.replace([to_replace, value, …])
DataFrame.replace
Replace values given in to_replace with value.
DataFrame.explode(column[, ignore_index])
DataFrame.explode
Transform each element of a list-like to a row, replicating index values.
DataFrame.melt([id_vars, value_vars, …])
DataFrame.melt
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.
DataFrame.sort_values(by[, axis, ascending, …])
DataFrame.sort_values
Sort by the values along either axis.
DataFrame.sort_index([axis, level, …])
DataFrame.sort_index
Sort object by labels (along an axis).
DataFrame.stack([level, dropna])
DataFrame.stack
Stack the prescribed level(s) from columns to index.
DataFrame.append(other[, ignore_index, …])
DataFrame.append
DataFrame.join(other[, on, how, lsuffix, …])
DataFrame.join
DataFrame.merge(right[, how, on, left_on, …])
DataFrame.merge
DataFrame.diff([periods, axis])
DataFrame.diff
First discrete difference of element.
DataFrame.shift([periods, freq, axis, …])
DataFrame.shift
Shift index by desired number of periods with an optional time freq.
DataFrame.tshift([periods, freq, axis])
DataFrame.tshift
Shift the time index, using the index’s frequency if available.
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.<kind>
alias of mars.dataframe.plotting.core.PlotAccessor
mars.dataframe.plotting.core.PlotAccessor
DataFrame.plot.area(*args, **kwargs)
DataFrame.plot.area
Draw a stacked area plot.
DataFrame.plot.bar(*args, **kwargs)
DataFrame.plot.bar
Vertical bar plot.
DataFrame.plot.barh(*args, **kwargs)
DataFrame.plot.barh
Make a horizontal bar plot.
DataFrame.plot.box(*args, **kwargs)
DataFrame.plot.box
Make a box plot of the DataFrame columns.
DataFrame.plot.density(*args, **kwargs)
DataFrame.plot.density
Generate Kernel Density Estimate plot using Gaussian kernels.
DataFrame.plot.hexbin(*args, **kwargs)
DataFrame.plot.hexbin
Generate a hexagonal binning plot.
DataFrame.plot.hist(*args, **kwargs)
DataFrame.plot.hist
Draw one histogram of the DataFrame’s columns.
DataFrame.plot.kde(*args, **kwargs)
DataFrame.plot.kde
DataFrame.plot.line(*args, **kwargs)
DataFrame.plot.line
Plot Series or DataFrame as lines.
DataFrame.plot.pie(*args, **kwargs)
DataFrame.plot.pie
Generate a pie plot.
DataFrame.plot.scatter(*args, **kwargs)
DataFrame.plot.scatter
Create a scatter plot with varying marker point size and color.
DataFrame.to_csv(path[, sep, na_rep, …])
DataFrame.to_csv
Write object to a comma-separated values (csv) file.
DataFrame.to_parquet(path[, engine, …])
DataFrame.to_parquet
Write a DataFrame to the binary parquet format, each chunk will be written to a Parquet file.
DataFrame.to_sql(name, con[, schema, …])
DataFrame.to_sql
Write records stored in a DataFrame to a SQL database.
DataFrame.map_chunk(func[, args])
DataFrame.map_chunk
Apply function to each chunk.
DataFrame.rebalance([factor, axis, …])
DataFrame.rebalance
Make Data more balanced across entire cluster.