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
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. |
DataFrame.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. |
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]) |
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. |