riccardotommasini/df4j

Dataframe For Java

Dataframe For Java

We report here the Dataframe documentation marking aspects already implemented.

Constructor

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

A DataFrame identifies a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).

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.ftypes Return the ftypes (indication of sparse/dense and dtype) in DataFrame.
• DataFrame.get_dtype_counts() Return counts of unique dtypes in this object.
• DataFrame.get_ftype_counts() (DEPRECATED) Return counts of unique ftypes in this object.
• DataFrame.select_dtypes([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.get_values() Return an ndarray after converting sparse values to dense.
• 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([index, deep]) Return the memory usage of each column in bytes.
• DataFrame.empty Indicator whether DataFrame is empty.
• DataFrame.is_copy Return the copy.

Conversion

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

Indexing, iteration

• DataFrame.head([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(loc, column, value[, …]) Insert column into DataFrame at specified location.
• DataFrame.__iter__() Iterate over infor axis
• DataFrame.items() Iterator over (column name, Series) pairs.
• DataFrame.keys() Get the ‘info axis’ (see Indexing for more)
• DataFrame.iteritems() Iterator over (column name, Series) pairs.
• DataFrame.iterrows() Iterate over DataFrame rows as (index, Series) pairs.
• DataFrame.itertuples([index, name]) Iterate over DataFrame rows as namedtuples.
• DataFrame.lookup(row_labels, col_labels) Label-based “fancy indexing” function for DataFrame.
• DataFrame.pop(item) Return item and drop from frame.
• DataFrame.tail([n]) Return the last n rows.
• DataFrame.xs(key[, axis, level, drop_level]) Return cross-section from the Series/DataFrame.
• DataFrame.get(key[, default]) Get item from object for given key (DataFrame column, Panel slice, etc.).
• DataFrame.isin(values) Whether each element in the DataFrame is contained in values.
• DataFrame.where(cond[, other, inplace, …]) Replace values where the condition is False.
• DataFrame.mask(cond[, other, inplace, axis, …]) Replace values where the condition is True.
• DataFrame.query(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(other[, axis, level, fill_value]) Addition of dataframe and other, element-wise (binary operator add).
• DataFrame.sub(other[, axis, level, fill_value]) Subtraction of dataframe and other, element-wise (binary operator sub).
• DataFrame.mul(other[, axis, level, fill_value]) Multiplication of dataframe and other, element-wise (binary operator mul).
• DataFrame.div(other[, axis, level, fill_value]) Floating division of dataframe and other, element-wise (binary operator truediv).
• DataFrame.truediv(other[, axis, level, …]) Floating division of dataframe and other, element-wise (binary operator truediv).
• DataFrame.floordiv(other[, axis, level, …]) Integer division of dataframe and other, element-wise (binary operator floordiv).
• DataFrame.mod(other[, axis, level, fill_value]) Modulo of dataframe and other, element-wise (binary operator mod).
• DataFrame.pow(other[, axis, level, fill_value]) Exponential power of dataframe and other, element-wise (binary operator pow).
• DataFrame.dot(other) Compute the matrix mutiplication between the DataFrame and other.
• DataFrame.radd(other[, axis, level, fill_value]) Addition of dataframe and other, element-wise (binary operator radd).
• DataFrame.rsub(other[, axis, level, fill_value]) Subtraction of dataframe and other, element-wise (binary operator rsub).
• DataFrame.rmul(other[, axis, level, fill_value]) Multiplication of dataframe and other, element-wise (binary operator rmul).
• DataFrame.rdiv(other[, axis, level, fill_value]) Floating division of dataframe and other, element-wise (binary operator rtruediv).
• DataFrame.rtruediv(other[, axis, level, …]) Floating division of dataframe and other, element-wise (binary operator rtruediv).
• DataFrame.rfloordiv(other[, axis, level, …]) Integer division of dataframe and other, element-wise (binary operator rfloordiv).
• DataFrame.rmod(other[, axis, level, fill_value]) Modulo of dataframe and other, element-wise (binary operator rmod).
• DataFrame.rpow(other[, axis, level, fill_value]) Exponential power of dataframe and other, element-wise (binary operator rpow).
• DataFrame.lt(other[, axis, level]) Less than of dataframe and other, element-wise (binary operator lt).
• DataFrame.gt(other[, axis, level]) Greater than of dataframe and other, element-wise (binary operator gt).
• DataFrame.le(other[, axis, level]) Less than or equal to of dataframe and other, element-wise (binary operator le).
• DataFrame.ge(other[, axis, level]) Greater than or equal to of dataframe and other, element-wise (binary operator ge).
• DataFrame.ne(other[, axis, level]) Not equal to of dataframe and other, element-wise (binary operator ne).
• DataFrame.eq(other[, axis, level]) Equal to of dataframe and other, element-wise (binary operator eq).
• DataFrame.combine(other, func[, fill_value, …]) Perform column-wise combine with another DataFrame based on a passed function.
• DataFrame.combine_first(other) Update null elements with value in the same location in other.

Function application, GroupBy & Window

• DataFrame.apply(func[, axis, broadcast, …]) Apply a function along an axis of the DataFrame.
• DataFrame.applymap(func) Apply a function to a Dataframe elementwise.
• DataFrame.pipe(func, *args, **kwargs) Apply func(self, *args, **kwargs).
• 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.transform(func[, axis]) Call func on self producing a DataFrame with transformed values and that has the same axis length as self.
• DataFrame.groupby([by, axis, level, …]) Group DataFrame or Series using a mapper or by a Series of columns.
• DataFrame.rolling(window[, min_periods, …]) Provides rolling window calculations.
• DataFrame.expanding([min_periods, center, axis]) Provides expanding transformations.
• DataFrame.ewm([com, span, halflife, alpha, …]) Provides exponential weighted functions. - [ ] Computations / Descriptive Stats
• DataFrame.abs() Return a Series/DataFrame with absolute numeric value of each element.
• DataFrame.all([axis, bool_only, skipna, level]) Return whether all elements are True, potentially over an axis.
• DataFrame.any([axis, bool_only, skipna, level]) Return whether any element is True, potentially over an axis.
• DataFrame.clip([lower, upper, axis, inplace]) Trim values at input threshold(s).
• DataFrame.clip_lower(threshold[, axis, inplace]) (DEPRECATED) Trim values below a given threshold.
• DataFrame.clip_upper(threshold[, axis, inplace]) (DEPRECATED) Trim values above a given threshold.
• DataFrame.compound([axis, skipna, level]) Return the compound percentage of the values for the requested axis.
• DataFrame.corr([method, min_periods]) Compute pairwise correlation of columns, excluding NA/null values.
• DataFrame.corrwith(other[, axis, drop, method]) Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame.
• DataFrame.count([axis, level, numeric_only]) Count non-NA cells for each column or row.
• DataFrame.cov([min_periods]) Compute pairwise covariance of columns, excluding NA/null values.
• DataFrame.cummax([axis, skipna]) Return cumulative maximum over a DataFrame or Series axis.
• DataFrame.cummin([axis, skipna]) Return cumulative minimum over a DataFrame or Series axis.
• DataFrame.cumprod([axis, skipna]) Return cumulative product over a DataFrame or Series axis.
• DataFrame.cumsum([axis, skipna]) Return cumulative sum over a DataFrame or Series axis.
• DataFrame.describe([percentiles, include, …]) Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
• DataFrame.diff([periods, axis]) First discrete difference of element.
• DataFrame.eval(expr[, inplace]) Evaluate a string describing operations on DataFrame columns.
• DataFrame.kurt([axis, skipna, level, …]) Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
• DataFrame.kurtosis([axis, skipna, level, …]) Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
• DataFrame.mad([axis, skipna, level]) Return the mean absolute deviation of the values for the requested axis.
• DataFrame.max([axis, skipna, level, …]) Return the maximum of the values for the requested axis.
• DataFrame.mean([axis, skipna, level, …]) Return the mean of the values for the requested axis.
• DataFrame.median([axis, skipna, level, …]) Return the median of the values for the requested axis.
• DataFrame.min([axis, skipna, level, …]) Return the minimum of the values for the requested axis.
• DataFrame.mode([axis, numeric_only, dropna]) Get the mode(s) of each element along the selected axis.
• DataFrame.pct_change([periods, fill_method, …]) Percentage change between the current and a prior element.
• DataFrame.prod([axis, skipna, level, …]) Return the product of the values for the requested axis.
• DataFrame.product([axis, skipna, level, …]) Return the product of the values for the requested axis.
• 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, …]) Return unbiased standard error of the mean over requested axis.
• DataFrame.skew([axis, skipna, level, …]) Return unbiased skew over requested axis Normalized by N-1.
• DataFrame.sum([axis, skipna, level, …]) Return the sum of the values for the requested axis.
• DataFrame.std([axis, skipna, level, ddof, …]) Return sample standard deviation over requested axis.
• DataFrame.var([axis, skipna, level, ddof, …]) Return unbiased variance over requested axis.
• DataFrame.nunique([axis, dropna]) Count distinct observations over requested axis.

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 for each axis Index.
• DataFrame.at_time(time[, asof, axis]) Select values at particular time of day (e.g.
• 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, optionally only considering certain columns.
• DataFrame.duplicated([subset, keep]) Return boolean Series denoting duplicate rows, optionally only considering certain columns.
• DataFrame.equals(other) Test whether two objects contain the same elements.
• DataFrame.filter([items, like, regex, axis]) Subset rows or columns of dataframe according to labels in the specified index.
• DataFrame.first(offset) Convenience method for subsetting initial periods of time series data based on a date offset.
• 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.last(offset) Convenience method for subsetting final periods of time series data based on a date offset.
• DataFrame.reindex([labels, index, columns, …]) Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
• DataFrame.reindex_axis(labels[, axis, …]) (DEPRECATED) Conform input object to new index.
• 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.select(crit[, axis]) (DEPRECATED) Return data corresponding to axis labels matching criteria.
• 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.tail([n]) Return the last n rows.
• DataFrame.take(indices[, axis, convert, is_copy]) 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.replace([to_replace, value, …]) Replace values given in to_replace with value.
• DataFrame.interpolate([method, axis, limit, …]) Interpolate values according to different methods.

Reshaping, sorting, transposing

• DataFrame.droplevel(level[, axis]) Return DataFrame with requested index / column level(s) removed.
• DataFrame.pivot([index, columns, 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.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.swaplevel([i, j, axis]) Swap levels i and j in a MultiIndex on a particular axis.
• DataFrame.stack([level, dropna]) Stack the prescribed level(s) from columns to index.
• DataFrame.unstack([level, fill_value]) Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.
• DataFrame.swapaxes(axis1, axis2[, copy]) Interchange axes and swap values axes appropriately.
• DataFrame.melt([id_vars, value_vars, …]) Unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set.
• DataFrame.squeeze([axis]) Squeeze 1 dimensional axis objects into scalars.
• DataFrame.to_panel() (DEPRECATED) Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
• DataFrame.to_xarray() Return an xarray object from the pandas object.
• DataFrame.T Transpose index and columns.
• DataFrame.transpose(*args, **kwargs) Transpose index and columns.

Combining / 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.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.asfreq(freq[, method, how, …]) Convert TimeSeries to specified frequency.
• DataFrame.asof(where[, subset]) Return the last row(s) without any NaNs before where.
• DataFrame.shift([periods, freq, axis, …]) Shift index by desired number of periods with an optional time freq.
• DataFrame.slice_shift([periods, axis]) Equivalent to shift without copying data.
• DataFrame.tshift([periods, freq, axis]) Shift the time index, using the index’s frequency if available.
• DataFrame.first_valid_index() Return index for first non-NA/null value.
• DataFrame.last_valid_index() Return index for last non-NA/null value.
• DataFrame.resample(rule[, how, axis, …]) Resample time-series data.
• DataFrame.to_period([freq, axis, copy]) Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed).
• DataFrame.to_timestamp([freq, how, axis, copy]) Cast to DatetimeIndex of timestamps, at beginning of period.
• DataFrame.tz_convert(tz[, axis, level, copy]) Convert tz-aware axis to target time zone.
• DataFrame.tz_localize(tz[, axis, level, …]) Localize tz-naive index of a Series or DataFrame to target time zone.

Plotting

• DataFrame.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot..
• DataFrame.plot([x, y, kind, ax, ….]) DataFrame plotting accessor and method
• DataFrame.plot.area([x, y]) Draw a stacked area plot.
• DataFrame.plot.bar([x, y]) Vertical bar plot.
• DataFrame.plot.barh([x, y]) Make a horizontal bar plot.
• DataFrame.plot.box([by]) Make a box plot of the DataFrame columns.
• DataFrame.plot.density([bw_method, ind]) Generate Kernel Density Estimate plot using Gaussian kernels.
• DataFrame.plot.hexbin(x, y[, C, …]) Generate a hexagonal binning plot.
• DataFrame.plot.hist([by, bins]) Draw one histogram of the DataFrame’s columns.
• DataFrame.plot.kde([bw_method, ind]) Generate Kernel Density Estimate plot using Gaussian kernels.
• DataFrame.plot.line([x, y]) Plot DataFrame columns as lines.
• DataFrame.plot.pie([y]) Generate a pie plot.
• DataFrame.plot.scatter(x, y[, s, c]) Create a scatter plot with varying marker point size and color.
• DataFrame.boxplot([column, by, ax, …]) Make a box plot from DataFrame columns.
• DataFrame.hist([column, by, grid, …]) Make a histogram of the DataFrame’s.

Serialization / IO / Conversion

• DataFrame.from_csv(path[, header, sep, …]) (DEPRECATED) Read CSV file.
• DataFrame.from_dict(data[, orient, dtype, …]) Construct DataFrame from dict of array-like or dicts.
• DataFrame.from_items(items[, columns, orient]) (DEPRECATED) Construct a DataFrame from a list of tuples.
• DataFrame.from_records(data[, index, …]) Convert structured or record ndarray to DataFrame.
• DataFrame.info([verbose, buf, max_cols, …]) Print a concise summary of a DataFrame.
• DataFrame.to_parquet(fname[, engine, …]) Write a DataFrame to the binary parquet format.
• DataFrame.to_pickle(path[, compression, …]) Pickle (serialize) object to file.
• DataFrame.to_csv([path_or_buf, sep, na_rep, …]) Write object to a comma-separated values (csv) file.
• DataFrame.to_hdf(path_or_buf, key, **kwargs) Write the contained data to an HDF5 file using HDFStore.
• DataFrame.to_sql(name, con[, schema, …]) Write records stored in a DataFrame to a SQL database.
• DataFrame.to_dict([orient, into]) Convert the DataFrame to a dictionary.
• DataFrame.to_excel(excel_writer[, …]) Write object to an Excel sheet.
• DataFrame.to_json([path_or_buf, orient, …]) Convert the object to a JSON string.
• DataFrame.to_html([buf, columns, col_space, …]) Render a DataFrame as an HTML table.
• DataFrame.to_feather(fname) Write out the binary feather-format for DataFrames.
• DataFrame.to_latex([buf, columns, …]) Render an object to a LaTeX tabular environment table.
• DataFrame.to_stata(fname[, convert_dates, …]) Export DataFrame object to Stata dta format.
• DataFrame.to_msgpack([path_or_buf, encoding]) Serialize object to input file path using msgpack format.
• DataFrame.to_gbq(destination_table[, …]) Write a DataFrame to a Google BigQuery table.
• DataFrame.to_records([index, …]) Convert DataFrame to a NumPy record array.
• DataFrame.to_sparse([fill_value, kind]) Convert to SparseDataFrame.
• DataFrame.to_dense() Return dense representation of NDFrame (as opposed to sparse).
• DataFrame.to_string([buf, columns, …]) Render a DataFrame to a console-friendly tabular output.
• DataFrame.to_clipboard([excel, sep]) Copy object to the system clipboard.
• DataFrame.style Property returning a Styler object containing methods for building a styled HTML representation fo the DataFrame.

Sparse

• SparseDataFrame.to_coo() Return the contents of the frame as a sparse SciPy COO matrix.