DataFrame

Constructor

DataFrame([data, index, columns, dtype, copy]) Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns).

Attributes and underlying data

Axes

  • index: row labels
  • columns: column labels
DataFrame.as_matrix([columns]) Convert the frame to its Numpy-array representation.
DataFrame.dtypes Return the dtypes in this object.
DataFrame.ftypes Return the ftypes (indication of sparse/dense and dtype) in this object.
DataFrame.get_dtype_counts() Return the counts of dtypes in this object.
DataFrame.get_ftype_counts() Return the counts of ftypes in this object.
DataFrame.select_dtypes([include, exclude]) Return a subset of a DataFrame including/excluding columns based on their dtype.
DataFrame.values Numpy representation of NDFrame
DataFrame.axes Return a list with the row axis labels and column axis labels as the only members.
DataFrame.ndim Number of axes / array dimensions
DataFrame.size number of elements in the NDFrame
DataFrame.shape Return a tuple representing the dimensionality of the DataFrame.
DataFrame.memory_usage([index, deep]) Memory usage of DataFrame columns.

Conversion

DataFrame.astype(dtype[, copy, raise_on_error]) Cast object to input numpy.dtype
DataFrame.convert_objects([convert_dates, ...]) Deprecated.
DataFrame.copy([deep]) Make a copy of this objects data.
DataFrame.isnull() Return a boolean same-sized object indicating if the values are null.
DataFrame.notnull() Return a boolean same-sized object indicating if the values are not null.

Indexing, iteration

DataFrame.head([n]) Returns first n rows
DataFrame.at Fast label-based scalar accessor
DataFrame.iat Fast integer location scalar accessor.
DataFrame.ix A primarily label-location based indexer, with integer position fallback.
DataFrame.loc Purely label-location based indexer for selection by label.
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.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, with index value as first element of the tuple.
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]) Returns last n rows
DataFrame.xs(key[, axis, level, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame.
DataFrame.isin(values) Return boolean DataFrame showing whether each element in the DataFrame is contained in values.
DataFrame.where(cond[, other, inplace, ...]) Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.
DataFrame.mask(cond[, other, inplace, axis, ...]) Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.
DataFrame.query(expr[, inplace]) Query the columns of a frame with a boolean expression.

For more information on .at, .iat, .ix, .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.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]) Wrapper for flexible comparison methods lt
DataFrame.gt(other[, axis, level]) Wrapper for flexible comparison methods gt
DataFrame.le(other[, axis, level]) Wrapper for flexible comparison methods le
DataFrame.ge(other[, axis, level]) Wrapper for flexible comparison methods ge
DataFrame.ne(other[, axis, level]) Wrapper for flexible comparison methods ne
DataFrame.eq(other[, axis, level]) Wrapper for flexible comparison methods eq
DataFrame.combine(other, func[, fill_value, ...]) Add two DataFrame objects and do not propagate NaN values, so if for a
DataFrame.combine_first(other) Combine two DataFrame objects and default to non-null values in frame calling the method.

Function application, GroupBy & Window

DataFrame.apply(func[, axis, broadcast, ...]) Applies function along input axis of DataFrame.
DataFrame.applymap(func) Apply a function to a DataFrame that is intended to operate elementwise, i.e.
DataFrame.groupby([by, axis, level, ...]) Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.
DataFrame.rolling(window[, min_periods, ...]) Provides rolling window calculcations.
DataFrame.expanding([min_periods, freq, ...]) Provides expanding transformations.
DataFrame.ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions

Computations / Descriptive Stats

DataFrame.abs() Return an object with absolute value taken–only applicable to objects that are all numeric.
DataFrame.all([axis, bool_only, skipna, level]) Return whether all elements are True over requested axis
DataFrame.any([axis, bool_only, skipna, level]) Return whether any element is True over requested axis
DataFrame.clip([lower, upper, axis]) Trim values at input threshold(s).
DataFrame.clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated.
DataFrame.clip_upper(threshold[, axis]) Return copy of input with values above given value(s) truncated.
DataFrame.corr([method, min_periods]) Compute pairwise correlation of columns, excluding NA/null values
DataFrame.corrwith(other[, axis, drop]) Compute pairwise correlation between rows or columns of two DataFrame objects.
DataFrame.count([axis, level, numeric_only]) Return Series with number of non-NA/null observations over requested axis.
DataFrame.cov([min_periods]) Compute pairwise covariance of columns, excluding NA/null values
DataFrame.cummax([axis, skipna]) Return cumulative max over requested axis.
DataFrame.cummin([axis, skipna]) Return cumulative minimum over requested axis.
DataFrame.cumprod([axis, skipna]) Return cumulative product over requested axis.
DataFrame.cumsum([axis, skipna]) Return cumulative sum over requested axis.
DataFrame.describe([percentiles, include, ...]) Generate various summary statistics, excluding NaN values.
DataFrame.diff([periods, axis]) 1st discrete difference of object
DataFrame.eval(expr[, inplace]) Evaluate an expression in the context of the calling DataFrame instance.
DataFrame.kurt([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, ...]) This method returns the maximum of the values in the object.
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, ...]) This method returns the minimum of the values in the object.
DataFrame.mode([axis, numeric_only]) Gets the mode(s) of each element along the axis selected.
DataFrame.pct_change([periods, fill_method, ...]) Percent change over given number of periods.
DataFrame.prod([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, a la numpy.percentile.
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
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.

Reindexing / Selection / Label manipulation

DataFrame.add_prefix(prefix) Concatenate prefix string with panel items names.
DataFrame.add_suffix(suffix) Concatenate suffix string with panel items names.
DataFrame.align(other[, join, axis, level, ...]) Align two object on their axes with the
DataFrame.drop(labels[, axis, level, ...]) Return new object with labels in requested axis removed.
DataFrame.drop_duplicates(*args, **kwargs) Return DataFrame with duplicate rows removed, optionally only
DataFrame.duplicated(*args, **kwargs) Return boolean Series denoting duplicate rows, optionally only
DataFrame.equals(other) Determines if two NDFrame 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]) Returns 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([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, ...]) Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index.
DataFrame.reindex_like(other[, method, ...]) Return an object with matching indices to myself.
DataFrame.rename([index, columns]) Alter axes input function or functions.
DataFrame.rename_axis(mapper[, axis, copy, ...]) Alter index and / or columns using input function or functions.
DataFrame.reset_index([level, drop, ...]) For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc.
DataFrame.sample([n, frac, replace, ...]) Returns a random sample of items from an axis of object.
DataFrame.select(crit[, axis]) Return data corresponding to axis labels matching criteria
DataFrame.set_index(keys[, drop, append, ...]) Set the DataFrame index (row labels) using one or more existing columns.
DataFrame.tail([n]) Returns last n rows
DataFrame.take(indices[, axis, convert, is_copy]) Analogous to ndarray.take
DataFrame.truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular index value.

Missing data handling

DataFrame.dropna([axis, how, thresh, ...]) Return object with labels on given axis omitted where alternately any
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’.

Reshaping, sorting, transposing

DataFrame.pivot([index, columns, values]) Reshape data (produce a “pivot” table) based on column values.
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.sortlevel([level, axis, ...]) Sort multilevel index by chosen axis and primary level.
DataFrame.nlargest(n, columns[, keep]) Get the rows of a DataFrame sorted by the n largest values of columns.
DataFrame.nsmallest(n, columns[, keep]) Get the rows of a DataFrame sorted by the n smallest values of columns.
DataFrame.swaplevel([i, j, axis]) Swap levels i and j in a MultiIndex on a particular axis
DataFrame.stack([level, dropna]) Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels.
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.T Transpose index and columns
DataFrame.to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.
DataFrame.to_xarray() Return an xarray object from the pandas object.
DataFrame.transpose(*args, **kwargs) Transpose index and columns

Combining / joining / merging

DataFrame.append(other[, ignore_index, ...]) Append rows of other to the end of this frame, returning a new object.
DataFrame.assign(**kwargs) Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones.
DataFrame.join(other[, on, how, lsuffix, ...]) Join columns with other DataFrame either on index or on a key column.
DataFrame.merge(right[, how, on, left_on, ...]) Merge DataFrame objects by performing a database-style join operation by columns or indexes.
DataFrame.update(other[, join, overwrite, ...]) Modify DataFrame in place using non-NA values from passed DataFrame.

Plotting

DataFrame.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot.<kind>.

DataFrame.plot([x, y, kind, ax, ....]) DataFrame plotting accessor and method
DataFrame.plot.area([x, y]) Area plot
DataFrame.plot.bar([x, y]) Vertical bar plot
DataFrame.plot.barh([x, y]) Horizontal bar plot
DataFrame.plot.box([by]) Boxplot
DataFrame.plot.density(**kwds) Kernel Density Estimate plot
DataFrame.plot.hexbin(x, y[, C, ...]) Hexbin plot
DataFrame.plot.hist([by, bins]) Histogram
DataFrame.plot.kde(**kwds) Kernel Density Estimate plot
DataFrame.plot.line([x, y]) Line plot
DataFrame.plot.pie([y]) Pie chart
DataFrame.plot.scatter(x, y[, s, c]) Scatter plot
DataFrame.boxplot([column, by, ax, ...]) Make a box plot from DataFrame column optionally grouped by some columns or
DataFrame.hist(data[, column, by, grid, ...]) Draw histogram of the DataFrame’s series using matplotlib / pylab.

Serialization / IO / Conversion

DataFrame.from_csv(path[, header, sep, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead).
DataFrame.from_dict(data[, orient, dtype]) Construct DataFrame from dict of array-like or dicts
DataFrame.from_items(items[, columns, orient]) Convert (key, value) pairs to DataFrame.
DataFrame.from_records(data[, index, ...]) Convert structured or record ndarray to DataFrame
DataFrame.info([verbose, buf, max_cols, ...]) Concise summary of a DataFrame.
DataFrame.to_pickle(path) Pickle (serialize) object to input file path.
DataFrame.to_csv([path_or_buf, sep, na_rep, ...]) Write DataFrame to a comma-separated values (csv) file
DataFrame.to_hdf(path_or_buf, key, **kwargs) Activate the HDFStore.
DataFrame.to_sql(name, con[, flavor, ...]) Write records stored in a DataFrame to a SQL database.
DataFrame.to_dict([orient]) Convert DataFrame to dictionary.
DataFrame.to_excel(excel_writer[, ...]) Write DataFrame to a 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_latex([buf, columns, ...]) Render a DataFrame to a tabular environment table.
DataFrame.to_stata(fname[, convert_dates, ...]) A class for writing Stata binary dta files from array-like objects
DataFrame.to_msgpack([path_or_buf, encoding]) msgpack (serialize) object to input file path
DataFrame.to_gbq(destination_table, project_id) Write a DataFrame to a Google BigQuery table.
DataFrame.to_records([index, convert_datetime64]) Convert DataFrame to 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]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.