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

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

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.info([verbose, buf, max_cols, ...])

Print a concise summary of a DataFrame.

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.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 Series/DataFrame is empty.

DataFrame.set_flags(*[, copy, ...])

Return a new object with updated flags.

DataFrame.astype(dtype[, copy, errors])

Cast a pandas object to a specified dtypedtype.

DataFrame.convert_dtypes([infer_objects, ...])

Convert columns to the best possible dtypes using dtypes supportingpd.NA.

DataFrame.infer_objects([copy])

Attempt to infer better dtypes for object columns.

DataFrame.copy([deep])

Make a copy of this object's indices and data.

DataFrame.bool()

(DEPRECATED) Return the bool of a single element Series or DataFrame.

DataFrame.to_numpy([dtype, copy, na_value])

Convert the DataFrame to a NumPy array.

DataFrame.head([n])

Return the firstn 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

(DEPRECATED) 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 info axis.

DataFrame.items()

Iterate over (column name, Series) pairs.

DataFrame.keys()

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

DataFrame.iterrows()

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

DataFrame.itertuples([index, name])

Iterate over DataFrame rows as namedtuples.

DataFrame.pop(item)

Return item and drop from frame.

DataFrame.tail([n])

Return the lastn 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 (ex: DataFrame column).

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.

DataFrame.__add__(other)

Get Addition of DataFrame and other, column-wise.

DataFrame.add(other[, axis, level, fill_value])

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

DataFrame.sub(other[, axis, level, fill_value])

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

DataFrame.mul(other[, axis, level, fill_value])

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

DataFrame.div(other[, axis, level, fill_value])

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

DataFrame.truediv(other[, axis, level, ...])

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

DataFrame.floordiv(other[, axis, level, ...])

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

DataFrame.mod(other[, axis, level, fill_value])

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

DataFrame.pow(other[, axis, level, fill_value])

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

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 operatorradd).

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

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

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

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

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

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

DataFrame.rtruediv(other[, axis, level, ...])

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

DataFrame.rfloordiv(other[, axis, level, ...])

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

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

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

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

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

DataFrame.lt(other[, axis, level])

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

DataFrame.gt(other[, axis, level])

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

DataFrame.le(other[, axis, level])

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

DataFrame.ge(other[, axis, level])

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

DataFrame.ne(other[, axis, level])

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

DataFrame.eq(other[, axis, level])

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

DataFrame.combine(other, func[, fill_value, ...])

Perform column-wise combine with another DataFrame.

DataFrame.combine_first(other)

Update null elements with value in the same location inother.

DataFrame.apply(func[, axis, raw, ...])

Apply a function along an axis of the DataFrame.

DataFrame.map(func[, na_action])

Apply a function to a Dataframe elementwise.

DataFrame.applymap(func[, na_action])

(DEPRECATED) Apply a function to a Dataframe elementwise.

DataFrame.pipe(func, *args, **kwargs)

Apply chainable functions that expect Series or DataFrames.

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])

Callfunc on self producing a DataFrame with the same axis shape as self.

DataFrame.groupby([by, axis, level, ...])

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

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

Provide rolling window calculations.

DataFrame.expanding([min_periods, axis, method])

Provide expanding window calculations.

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

Provide exponentially weighted (EW) calculations.

DataFrame.abs()

Return a Series/DataFrame with absolute numeric value of each element.

DataFrame.all([axis, bool_only, skipna])

Return whether all elements are True, potentially over an axis.

DataFrame.any(*[, axis, bool_only, skipna])

Return whether any element is True, potentially over an axis.

DataFrame.clip([lower, upper, axis, inplace])

Trim values at input threshold(s).

DataFrame.corr([method, min_periods, ...])

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

DataFrame.corrwith(other[, axis, drop, ...])

Compute pairwise correlation.

DataFrame.count([axis, numeric_only])

Count non-NA cells for each column or row.

DataFrame.cov([min_periods, ddof, numeric_only])

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.

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, numeric_only])

Return unbiased kurtosis over requested axis.

DataFrame.kurtosis([axis, skipna, numeric_only])

Return unbiased kurtosis over requested axis.

DataFrame.max([axis, skipna, numeric_only])

Return the maximum of the values over the requested axis.

DataFrame.mean([axis, skipna, numeric_only])

Return the mean of the values over the requested axis.

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

Return the median of the values over the requested axis.

DataFrame.min([axis, skipna, numeric_only])

Return the minimum of the values over 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, ...])

Fractional change between the current and a prior element.

DataFrame.prod([axis, skipna, numeric_only, ...])

Return the product of the values over the requested axis.

DataFrame.product([axis, skipna, ...])

Return the product of the values over 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, ddof, numeric_only])

Return unbiased standard error of the mean over requested axis.

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

Return unbiased skew over requested axis.

DataFrame.sum([axis, skipna, numeric_only, ...])

Return the sum of the values over the requested axis.

DataFrame.std([axis, skipna, ddof, numeric_only])

Return sample standard deviation over requested axis.

DataFrame.var([axis, skipna, ddof, numeric_only])

Return unbiased variance over requested axis.

DataFrame.nunique([axis, dropna])

Count number of distinct elements in specified axis.

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

Return a Series containing the frequency of each distinct row in the Dataframe.

DataFrame.add_prefix(prefix[, axis])

Prefix labels with stringprefix.

DataFrame.add_suffix(suffix[, axis])

Suffix labels with stringsuffix.

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

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

DataFrame.at_time(time[, asof, axis])

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

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.

DataFrame.duplicated([subset, keep])

Return boolean Series denoting duplicate rows.

DataFrame.equals(other)

Test whether two objects contain the same elements.

DataFrame.filter([items, like, regex, axis])

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

DataFrame.first(offset)

(DEPRECATED) Select initial periods of time series data based on a date offset.

DataFrame.head([n])

Return the firstn rows.

DataFrame.idxmax([axis, skipna, numeric_only])

Return index of first occurrence of maximum over requested axis.

DataFrame.idxmin([axis, skipna, numeric_only])

Return index of first occurrence of minimum over requested axis.

DataFrame.last(offset)

(DEPRECATED) Select 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.

DataFrame.reindex_like(other[, method, ...])

Return an object with matching indices as other object.

DataFrame.rename([mapper, index, columns, ...])

Rename columns or index 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, copy])

Assign desired index to given axis.

DataFrame.set_index(keys, *[, drop, append, ...])

Set the DataFrame index using existing columns.

DataFrame.tail([n])

Return the lastn rows.

DataFrame.take(indices[, axis])

Return the elements in the givenpositional indices along an axis.

DataFrame.truncate([before, after, axis, copy])

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

DataFrame.backfill(*[, axis, inplace, ...])

(DEPRECATED) Fill NA/NaN values by using the next valid observation to fill the gap.

DataFrame.bfill(*[, axis, inplace, limit, ...])

Fill NA/NaN values by using the next valid observation to fill the gap.

DataFrame.dropna(*[, axis, how, thresh, ...])

Remove missing values.

DataFrame.ffill(*[, axis, inplace, limit, ...])

Fill NA/NaN values by propagating the last valid observation to next valid.

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

Fill NA/NaN values using the specified method.

DataFrame.interpolate([method, axis, limit, ...])

Fill NaN values using an interpolation method.

DataFrame.isna()

Detect missing values.

DataFrame.isnull()

DataFrame.isnull is an alias for DataFrame.isna.

DataFrame.notna()

Detect existing (non-missing) values.

DataFrame.notnull()

DataFrame.notnull is an alias for DataFrame.notna.

DataFrame.pad(*[, axis, inplace, limit, ...])

(DEPRECATED) Fill NA/NaN values by propagating the last valid observation to next valid.

DataFrame.replace([to_replace, value, ...])

Replace values given into_replace withvalue.

DataFrame.droplevel(level[, axis])

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

DataFrame.pivot(*, columns[, index, 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, ...])

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 firstn rows ordered bycolumns in descending order.

DataFrame.nsmallest(n, columns[, keep])

Return the firstn rows ordered bycolumns in ascending order.

DataFrame.swaplevel([i, j, axis])

Swap levels i and j in aMultiIndex.

DataFrame.stack([level, dropna, sort, ...])

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

DataFrame.unstack([level, fill_value, sort])

Pivot a level of the (necessarily hierarchical) index labels.

DataFrame.swapaxes(axis1, axis2[, copy])

(DEPRECATED) Interchange axes and swap values axes appropriately.

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

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

DataFrame.explode(column[, ignore_index])

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

DataFrame.squeeze([axis])

Squeeze 1 dimensional axis objects into scalars.

DataFrame.to_xarray()

Return an xarray object from the pandas object.

DataFrame.T

The transpose of the DataFrame.

DataFrame.transpose(*args[, copy])

Transpose index and columns.

DataFrame.assign(**kwargs)

Assign new columns to a DataFrame.

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

Compare to another DataFrame and show the differences.

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.

DataFrame.asfreq(freq[, method, how, ...])

Convert time series to specified frequency.

DataFrame.asof(where[, subset])

Return the last row(s) without any NaNs beforewhere.

DataFrame.shift([periods, freq, axis, ...])

Shift index by desired number of periods with an optional timefreq.

DataFrame.first_valid_index()

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

DataFrame.last_valid_index()

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

DataFrame.resample(rule[, axis, closed, ...])

Resample time-series data.

DataFrame.to_period([freq, axis, copy])

Convert DataFrame from DatetimeIndex to PeriodIndex.

DataFrame.to_timestamp([freq, how, axis, copy])

Cast to DatetimeIndex of timestamps, atbeginning 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.

Flags(obj, *, allows_duplicate_labels)

Flags that apply to pandas objects.

DataFrame.attrs

Dictionary of global attributes of this dataset.

DataFrame.plot([x, y, kind, ax, ....])

DataFrame plotting accessor and method

DataFrame.plot.area([x, y, stacked])

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 Series or DataFrame as lines.

DataFrame.plot.pie(**kwargs)

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 columns.

DataFrame.sparse.density

Ratio of non-sparse points to total (dense) data points.

DataFrame.sparse.from_spmatrix(data[, ...])

Create a new DataFrame from a scipy sparse matrix.

DataFrame.sparse.to_coo()

Return the contents of the frame as a sparse SciPy COO matrix.

DataFrame.sparse.to_dense()

Convert a DataFrame with sparse values to dense.

DataFrame.from_dict(data[, orient, dtype, ...])

Construct DataFrame from dict of array-like or dicts.

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

Convert structured or record ndarray to DataFrame.

DataFrame.to_orc([path, engine, index, ...])

Write a DataFrame to the ORC format.

DataFrame.to_parquet([path, 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[, ...])

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, index])

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(path, **kwargs)

Write a DataFrame to the binary Feather format.

DataFrame.to_latex([buf, columns, header, ...])

Render object to a LaTeX tabular, longtable, or nested table.

DataFrame.to_stata(path, *[, convert_dates, ...])

Export DataFrame object to Stata dta format.

DataFrame.to_gbq(destination_table, *[, ...])

(DEPRECATED) Write a DataFrame to a Google BigQuery table.

DataFrame.to_records([index, column_dtypes, ...])

Convert DataFrame to a NumPy record array.

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.to_markdown([buf, mode, index, ...])

Print DataFrame in Markdown-friendly format.

DataFrame.style

Returns a Styler object.

DataFrame.__dataframe__([nan_as_null, ...])

Return the dataframe interchange object implementing the interchange protocol.