DataFrame

Constructor

DataFrame([data, index, columns, dtype, copy]) Koala DataFrame that corresponds to Pandas DataFrame logically.

Attributes and underlying data

DataFrame.index The index (row labels) Column of the DataFrame.
DataFrame.columns The column labels of the DataFrame.
DataFrame.empty Returns true if the current DataFrame is empty.
DataFrame.dtypes Return the dtypes in the DataFrame.
DataFrame.shape Return a tuple representing the dimensionality of the DataFrame.
DataFrame.size Return an int representing the number of elements in this object.

Conversion

DataFrame.copy(self) Make a copy of this object’s indices and data.
DataFrame.isna(self) Detects missing values for items in the current Dataframe.
DataFrame.astype(self, dtype) Cast a pandas object to a specified dtype dtype.
DataFrame.isnull(self) Detects missing values for items in the current Dataframe.
DataFrame.notna(self) Detects non-missing values for items in the current Dataframe.
DataFrame.notnull(self) Detects non-missing values for items in the current Dataframe.

Indexing, iteration

DataFrame.head(self[, n]) Return the first n rows.
DataFrame.loc Access a group of rows and columns by label(s) or a boolean Series.
DataFrame.iloc Purely integer-location based indexing for selection by position.
DataFrame.iteritems(self) Iterator over (column name, Series) pairs.
DataFrame.get(self, key[, default]) Get item from object for given key (DataFrame column, Panel slice, etc.).

Function application, GroupBy & Window

DataFrame.applymap(self, func) Apply a function to a Dataframe elementwise.
DataFrame.pipe(self, func, \*args, \*\*kwargs) Apply func(self, *args, **kwargs).
DataFrame.groupby(self, by) Group DataFrame or Series using a Series of columns.

Computations / Descriptive Stats

DataFrame.abs(self) Return a Series/DataFrame with absolute numeric value of each element.
DataFrame.clip(self, lower, int]=None, …) Trim values at input threshold(s).
DataFrame.corr(self[, method]) Compute pairwise correlation of columns, excluding NA/null values.
DataFrame.count(self) Count non-NA cells for each column.
DataFrame.describe(self, percentiles, …) Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
DataFrame.kurt(self) Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.kurtosis(self) Return unbiased kurtosis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
DataFrame.max(self) Return the maximum of the values.
DataFrame.mean(self) Return the mean of the values.
DataFrame.min(self) Return the minimum of the values.
DataFrame.skew(self) Return unbiased skew normalized by N-1.
DataFrame.sum(self) Return the sum of the values.
DataFrame.std(self) Return sample standard deviation.
DataFrame.var(self) Return unbiased variance.

Reindexing / Selection / Label manipulation

DataFrame.drop(self[, labels, axis]) Drop specified labels from columns.
DataFrame.head(self[, n]) Return the first n rows.
DataFrame.reset_index(self[, level, drop, …]) Reset the index, or a level of it.
DataFrame.set_index(self, keys[, drop, …]) Set the DataFrame index (row labels) using one or more existing columns.
DataFrame.isin(self, values) Whether each element in the DataFrame is contained in values.
DataFrame.sample(self, n, NoneType]=None, …) Return a random sample of items from an axis of object.

Missing data handling

DataFrame.dropna(self[, axis, how, thresh, …]) Remove missing values.
DataFrame.fillna(self[, value, axis, inplace]) Fill NA/NaN values.

Reshaping, sorting, transposing

DataFrame.sort_index(self, axis, level, …) Sort object by labels (along an axis)
DataFrame.sort_values(self, by, List[str]], …) Sort by the values along either axis.

Combining / joining / merging

DataFrame.assign(self, \*\*kwargs) Assign new columns to a DataFrame.
DataFrame.merge(left, right, how, on, …) Merge DataFrame objects with a database-style join.

Serialization / IO / Conversion

DataFrame.to_csv(self[, path_or_buf, sep, …]) Write object to a comma-separated values (csv) file.
DataFrame.to_pandas(self) Return a Pandas DataFrame.
DataFrame.to_html(self[, buf, columns, …]) Render a DataFrame as an HTML table.
DataFrame.to_numpy(self) A NumPy ndarray representing the values in this DataFrame or Series.
DataFrame.to_koalas(self) Converts the existing DataFrame into a Koalas DataFrame.
DataFrame.to_spark(self) Return the current DataFrame as a Spark DataFrame.
DataFrame.to_string(self[, buf, columns, …]) Render a DataFrame to a console-friendly tabular output.
DataFrame.to_json(self[, path_or_buf, …]) Convert the object to a JSON string.
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_clipboard(self[, excel, sep]) Copy object to the system clipboard.
DataFrame.to_records(self[, index, …]) Convert DataFrame to a NumPy record array.
DataFrame.to_latex(self[, buf, columns, …]) Render an object to a LaTeX tabular environment table.
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