maxframe.dataframe.DataFrame#

class maxframe.dataframe.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False, chunk_size=None, gpu=None, sparse=None, num_partitions=None, dtype_backend=None)[source]#
__init__(data=None, index=None, columns=None, dtype=None, copy=False, chunk_size=None, gpu=None, sparse=None, num_partitions=None, dtype_backend=None)[source]#

Methods

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

abs()

add(other[, axis, level, fill_value])

Get Addition of dataframe and other, element-wise (binary operator add).

add_prefix(prefix)

Prefix labels with string prefix.

add_suffix(suffix)

Suffix labels with string suffix.

agg([func, axis])

Aggregate using one or more operations over the specified axis.

aggregate([func, axis])

Aggregate using one or more operations over the specified axis.

align(other[, join, axis, level, copy, ...])

Align two objects on their axes with the specified join method.

all([axis, bool_only, skipna, level, method])

any([axis, bool_only, skipna, level, method])

append(other[, ignore_index, ...])

Append rows of other to the end of caller, returning a new object.

apply(func[, axis, raw, result_type, args, ...])

Apply a function along an axis of the DataFrame.

applymap(func[, na_action, dtypes, dtype, ...])

Apply a function to a Dataframe elementwise.

around([decimals])

Round a DataFrame to a variable number of decimal places.

assign(**kwargs)

Assign new columns to a DataFrame.

astype(dtype[, copy, errors])

Cast a pandas object to a specified dtype dtype.

at_time(time[, axis])

Select values at particular time of day (e.g., 9:30AM).

backfill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='bfill'.

between_time(start_time, end_time[, ...])

Select values between particular times of the day (e.g., 9:00-9:30 AM).

bfill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='bfill'.

clip([lower, upper, axis, inplace])

Trim values at input threshold(s).

combine(other, func[, fill_value, overwrite])

Perform column-wise combine with another DataFrame.

combine_first(other)

Update null elements with value in the same location in other.

compare(other[, align_axis, keep_shape, ...])

Compare to another DataFrame and show the differences.

convert_dtypes([infer_objects, ...])

Convert columns to best possible dtypes using dtypes supporting pd.NA.

copy()

copy_from(obj)

copy_to(target)

corr([method, min_periods])

Compute pairwise correlation of columns, excluding NA/null values.

corrwith(other[, axis, drop, method])

Compute pairwise correlation.

count([axis, level, numeric_only])

cov([min_periods, ddof, numeric_only])

Compute pairwise covariance of columns, excluding NA/null values.

cummax([axis, skipna])

cummin([axis, skipna])

cumprod([axis, skipna])

cumsum([axis, skipna])

describe([percentiles, include, exclude])

Generate descriptive statistics.

diff([periods, axis])

First discrete difference of element.

div(other[, axis, level, fill_value])

Get Floating division of dataframe and other, element-wise (binary operator truediv).

dot(other)

Compute the matrix multiplication between the DataFrame and other.

drop([labels, axis, index, columns, level, ...])

Drop specified labels from rows or columns.

drop_duplicates([subset, keep, inplace, ...])

Return DataFrame with duplicate rows removed.

droplevel(level[, axis])

Return Series/DataFrame with requested index / column level(s) removed.

dropna([axis, how, thresh, subset, inplace, ...])

Remove missing values.

duplicated([subset, keep, method])

Return boolean Series denoting duplicate rows.

eq(other[, axis, level, fill_value])

Get Equal to of dataframe and other, element-wise (binary operator eq).

eval(expr[, inplace])

Evaluate a string describing operations on DataFrame columns.

ewm([com, span, halflife, alpha, ...])

Provide exponential weighted functions.

execute([session])

expanding([min_periods, shift, reverse_range])

Provide expanding transformations.

explode(column[, ignore_index, ...])

Transform each element of a list-like to a row, replicating index values.

ffill([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='ffill'.

fillna([value, method, axis, inplace, ...])

Fill NA/NaN values using the specified method.

filter([items, like, regex, axis])

Subset the dataframe rows or columns according to the specified index labels.

first_valid_index()

Return index for first non-NA value or None, if no non-NA value is found.

floordiv(other[, axis, level, fill_value])

Get Integer division of dataframe and other, element-wise (binary operator floordiv).

from_dict(data[, orient, dtype, columns])

Construct DataFrame from dict of array-like or dicts.

from_records(data[, index, exclude, ...])

Convert structured or record ndarray to DataFrame.

from_tensor(tensor[, index, columns, gpu, ...])

ge(other[, axis, level, fill_value])

Get Greater than or equal to of dataframe and other, element-wise (binary operator ge).

groupby([by, level, as_index, sort, ...])

Group DataFrame using a mapper or by a Series of columns.

gt(other[, axis, level, fill_value])

Get Greater than of dataframe and other, element-wise (binary operator gt).

head([n])

Return the first n rows.

idxmax([axis, skipna])

Return index of first occurrence of maximum over requested axis.

idxmin([axis, skipna])

Return index of first occurrence of minimum over requested axis.

infer_objects([copy])

Attempt to infer better dtypes for object columns.

insert(loc, column, value[, allow_duplicates])

Insert column into DataFrame at specified location.

isin(values)

Whether each element in the DataFrame is contained in values.

isna()

Detect missing values.

isnull()

Detect missing values.

iterrows([batch_size, session])

Iterate over DataFrame rows as (index, Series) pairs.

itertuples([index, name, batch_size, session])

Iterate over DataFrame rows as namedtuples.

join(other[, on, how, lsuffix, rsuffix, ...])

Join columns of another DataFrame.

keys()

Get the 'info axis' (see Indexing for more).

kurt([axis, skipna, level, numeric_only, ...])

kurtosis([axis, skipna, level, ...])

last_valid_index()

Return index for last non-NA value or None, if no non-NA value is found.

le(other[, axis, level, fill_value])

Get Less than or equal to of dataframe and other, element-wise (binary operator le).

lt(other[, axis, level, fill_value])

Get Less than of dataframe and other, element-wise (binary operator lt).

map(func[, na_action, dtypes, dtype, skip_infer])

Apply a function to a Dataframe elementwise.

mask(cond[, other, inplace, axis, level, ...])

Replace values where the condition is True.

max([axis, skipna, level, numeric_only, method])

mean([axis, skipna, level, numeric_only, method])

median([axis, skipna, level, numeric_only, ...])

melt([id_vars, value_vars, var_name, ...])

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

memory_usage([index, deep])

Return the memory usage of each column in bytes.

merge(right[, how, on, left_on, right_on, ...])

Merge DataFrame or named Series objects with a database-style join.

min([axis, skipna, level, numeric_only, method])

mod(other[, axis, level, fill_value])

Get Modulo of dataframe and other, element-wise (binary operator mod).

mode([axis, numeric_only, dropna, combine_size])

Get the mode(s) of each element along the selected axis.

mul(other[, axis, level, fill_value])

Get Multiplication of dataframe and other, element-wise (binary operator mul).

multiply(other[, axis, level, fill_value])

Get Multiplication of dataframe and other, element-wise (binary operator mul).

ne(other[, axis, level, fill_value])

Get Not equal to of dataframe and other, element-wise (binary operator ne).

nlargest(n, columns[, keep])

Return the first n rows ordered by columns in descending order.

notna()

Detect existing (non-missing) values.

notnull()

Detect existing (non-missing) values.

nsmallest(n, columns[, keep])

Return the first n rows ordered by columns in ascending order.

nunique([axis, dropna])

Count distinct observations over requested axis.

pad([axis, inplace, limit, downcast])

Synonym for DataFrame.fillna() with method='ffill'.

pct_change([periods, fill_method, limit, freq])

Percentage change between the current and a prior element.

pivot(columns[, index, values])

Return reshaped DataFrame organized by given index / column values.

pivot_table([values, index, columns, ...])

Create a spreadsheet-style pivot table as a DataFrame.

pop(item)

Return item and drop from frame.

pow(other[, axis, level, fill_value])

Get Exponential power of dataframe and other, element-wise (binary operator pow).

prod([axis, skipna, level, min_count, ...])

product([axis, skipna, level, min_count, ...])

quantile([q, axis, numeric_only, interpolation])

Return values at the given quantile over requested axis.

query(expr[, inplace])

Query the columns of a DataFrame with a boolean expression.

radd(other[, axis, level, fill_value])

Get Addition of dataframe and other, element-wise (binary operator radd).

rank([axis, method, numeric_only, ...])

Compute numerical data ranks (1 through n) along axis.

rdiv(other[, axis, level, fill_value])

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

rechunk(chunk_size[, reassign_worker])

Rechunk DataFrame, Series or Index data.

reindex([labels, index, columns, axis, ...])

Conform Series/DataFrame to new index with optional filling logic.

reindex_like(other[, method, copy, limit, ...])

Return an object with matching indices as other object.

rename([mapper, index, columns, axis, copy, ...])

Alter axes labels.

rename_axis([mapper, index, columns, axis, ...])

Set the name of the axis for the index or columns.

reorder_levels(order[, axis])

Rearrange index levels using input order.

replace([to_replace, value, inplace, limit, ...])

Replace values given in to_replace with value.

reset_index([level, drop, inplace, ...])

Reset the index, or a level of it.

rfloordiv(other[, axis, level, fill_value])

Get Integer division of dataframe and other, element-wise (binary operator rfloordiv).

rmod(other[, axis, level, fill_value])

Get Modulo of dataframe and other, element-wise (binary operator rmod).

rmul(other[, axis, level, fill_value])

Get Multiplication of dataframe and other, element-wise (binary operator rmul).

rolling(window[, min_periods, center, ...])

Provide rolling window calculations.

round([decimals])

Round a DataFrame to a variable number of decimal places.

rpow(other[, axis, level, fill_value])

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

rsub(other[, axis, level, fill_value])

Get Subtraction of dataframe and other, element-wise (binary operator rsubtract).

rtruediv(other[, axis, level, fill_value])

Get Floating division of dataframe and other, element-wise (binary operator rtruediv).

sample([n, frac, replace, weights, ...])

Return a random sample of items from an axis of object.

select_dtypes([include, exclude])

Return a subset of the DataFrame's columns based on the column dtypes.

sem([axis, skipna, level, ddof, ...])

set_axis(labels[, axis, inplace])

Assign desired index to given axis.

set_index(keys[, drop, append, inplace, ...])

Set the DataFrame index using existing columns.

shift([periods, freq, axis, fill_value])

Shift index by desired number of periods with an optional time freq.

skew([axis, skipna, level, numeric_only, ...])

sort_index([axis, level, ascending, ...])

Sort object by labels (along an axis).

sort_values(by[, axis, ascending, inplace, ...])

Sort by the values along either axis.

stack([level, dropna])

Stack the prescribed level(s) from columns to index.

std([axis, skipna, level, ddof, ...])

sub(other[, axis, level, fill_value])

Get Subtraction of dataframe and other, element-wise (binary operator subtract).

sum([axis, skipna, level, min_count, ...])

swaplevel([i, j, axis])

Swap levels i and j in a MultiIndex.

tail([n])

Return the last n rows.

take(indices[, axis])

Return the elements in the given positional indices along an axis.

to_clipboard(*[, excel, sep, batch_size, ...])

Copy object to the system clipboard.

to_csv(path[, sep, na_rep, float_format, ...])

Write object to a comma-separated values (csv) file.

to_dict([orient, into, index, batch_size, ...])

Convert the DataFrame to a dictionary.

to_json([path, orient, date_format, ...])

Convert the object to a JSON string.

to_lance(path[, mode, index, index_label, ...])

Write a DataFrame to a Lance dataset.

to_odps_table(table[, partition, ...])

Write DataFrame object into a MaxCompute (ODPS) table.

to_pandas([session])

to_parquet(path[, engine, compression, ...])

Write a DataFrame to the binary parquet format, each chunk will be written to a Parquet file.

to_tensor()

transform(func[, axis, dtypes, skip_infer])

Call func on self producing a DataFrame with transformed values.

transpose()

Transpose index and columns.

truediv(other[, axis, level, fill_value])

Get Floating division of dataframe and other, element-wise (binary operator truediv).

truncate([before, after, axis, copy])

Truncate a Series or DataFrame before and after some index value.

tshift([periods, freq, axis])

Shift the time index, using the index's frequency if available.

unstack([level, fill_value])

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

update(other[, join, overwrite, ...])

Modify in place using non-NA values from another DataFrame.

value_counts([subset, normalize, sort, ...])

var([axis, skipna, level, ddof, ...])

where(cond[, other, inplace, axis, level, ...])

Replace values where the condition is False.

xs(key[, axis, level, drop_level])

Return cross-section from the Series/DataFrame.

Attributes

T

at

Access a single value for a row/column label pair.

columns

data

dtypes

Return the dtypes in the DataFrame.

iat

Access a single value for a row/column pair by integer position.

iloc

Purely integer-location based indexing for selection by position.

index

loc

Access a group of rows and columns by label(s) or a boolean array.

ndim

Return an int representing the number of axes / array dimensions.

shape

size

type_name

values