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- Constructor
- Attributes and underlying data
- Conversion
- Indexing, iteration
- Binary operator functions
- Function application, GroupBy & window
- Computations / descriptive stats
- Reindexing / selection / label manipulation
- Missing data handling
- Reshaping, sorting, transposing
- Combining / joining / merging
- Time series-related
- Plotting
- Sparse accessor
- Serialization / IO / conversion
- Sparse
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 | (DEPRECATED) Return the ftypes (indication of sparse/dense and dtype) in DataFrame. |
DataFrame.get_dtype_counts(self) | (DEPRECATED) Return counts of unique dtypes in this object. |
DataFrame.get_ftype_counts(self) | (DEPRECATED) Return counts of unique ftypes in this object. |
DataFrame.select_dtypes(self[, 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(self) | (DEPRECATED) 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(self[, 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(self, dtype[, copy, errors]) | Cast a pandas object to a specified dtype dtype. |
DataFrame.infer_objects(self) | Attempt to infer better dtypes for object columns. |
DataFrame.copy(self[, deep]) | Make a copy of this object’s indices and data. |
DataFrame.isna(self) | Detect missing values. |
DataFrame.notna(self) | Detect existing (non-missing) values. |
DataFrame.bool(self) | Return the bool of a single element PandasObject. |
Indexing, iteration
DataFrame.head(self[, 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(self, loc, column, value[, …]) | Insert column into DataFrame at specified location. |
DataFrame.iter(self) | Iterate over info axis. |
DataFrame.items(self) | Iterator over (column name, Series) pairs. |
DataFrame.iteritems(self) | Iterator over (column name, Series) pairs. |
DataFrame.keys(self) | Get the ‘info axis’ (see Indexing for more) |
DataFrame.iterrows(self) | Iterate over DataFrame rows as (index, Series) pairs. |
DataFrame.itertuples(self[, index, name]) | Iterate over DataFrame rows as namedtuples. |
DataFrame.lookup(self, rowlabels, col_labels) | Label-based “fancy indexing” function for DataFrame. |
DataFrame.pop(self, item) | Return item and drop from frame. |
DataFrame.tail(self[, n]) | Return the last _n rows. |
DataFrame.xs(self, key[, axis, level, …]) | Return cross-section from the Series/DataFrame. |
DataFrame.get(self, key[, default]) | Get item from object for given key (ex: DataFrame column). |
DataFrame.isin(self, values) | Whether each element in the DataFrame is contained in values. |
DataFrame.where(self, cond[, other, …]) | Replace values where the condition is False. |
DataFrame.mask(self, cond[, other, inplace, …]) | Replace values where the condition is True. |
DataFrame.query(self, 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(self, other[, axis, level, …]) | Get Addition of dataframe and other, element-wise (binary operator add). |
DataFrame.sub(self, other[, axis, level, …]) | Get Subtraction of dataframe and other, element-wise (binary operator sub). |
DataFrame.mul(self, other[, axis, level, …]) | Get Multiplication of dataframe and other, element-wise (binary operator mul). |
DataFrame.div(self, other[, axis, level, …]) | Get Floating division of dataframe and other, element-wise (binary operator truediv). |
DataFrame.truediv(self, other[, axis, …]) | Get Floating division of dataframe and other, element-wise (binary operator truediv). |
DataFrame.floordiv(self, other[, axis, …]) | Get Integer division of dataframe and other, element-wise (binary operator floordiv). |
DataFrame.mod(self, other[, axis, level, …]) | Get Modulo of dataframe and other, element-wise (binary operator mod). |
DataFrame.pow(self, other[, axis, level, …]) | Get Exponential power of dataframe and other, element-wise (binary operator pow). |
DataFrame.dot(self, other) | Compute the matrix multiplication between the DataFrame and other. |
DataFrame.radd(self, other[, axis, level, …]) | Get Addition of dataframe and other, element-wise (binary operator radd). |
DataFrame.rsub(self, other[, axis, level, …]) | Get Subtraction of dataframe and other, element-wise (binary operator rsub). |
DataFrame.rmul(self, other[, axis, level, …]) | Get Multiplication of dataframe and other, element-wise (binary operator rmul). |
DataFrame.rdiv(self, other[, axis, level, …]) | Get Floating division of dataframe and other, element-wise (binary operator rtruediv). |
DataFrame.rtruediv(self, other[, axis, …]) | Get Floating division of dataframe and other, element-wise (binary operator rtruediv). |
DataFrame.rfloordiv(self, other[, axis, …]) | Get Integer division of dataframe and other, element-wise (binary operator rfloordiv). |
DataFrame.rmod(self, other[, axis, level, …]) | Get Modulo of dataframe and other, element-wise (binary operator rmod). |
DataFrame.rpow(self, other[, axis, level, …]) | Get Exponential power of dataframe and other, element-wise (binary operator rpow). |
DataFrame.lt(self, other[, axis, level]) | Get Less than of dataframe and other, element-wise (binary operator lt). |
DataFrame.gt(self, other[, axis, level]) | Get Greater than of dataframe and other, element-wise (binary operator gt). |
DataFrame.le(self, other[, axis, level]) | Get Less than or equal to of dataframe and other, element-wise (binary operator le). |
DataFrame.ge(self, other[, axis, level]) | Get Greater than or equal to of dataframe and other, element-wise (binary operator ge). |
DataFrame.ne(self, other[, axis, level]) | Get Not equal to of dataframe and other, element-wise (binary operator ne). |
DataFrame.eq(self, other[, axis, level]) | Get Equal to of dataframe and other, element-wise (binary operator eq). |
DataFrame.combine(self, other, func[, …]) | Perform column-wise combine with another DataFrame. |
DataFrame.combine_first(self, other) | Update null elements with value in the same location in other. |
Function application, GroupBy & window
DataFrame.apply(self, func[, axis, …]) | Apply a function along an axis of the DataFrame. |
DataFrame.applymap(self, func) | Apply a function to a Dataframe elementwise. |
DataFrame.pipe(self, func, *args, **kwargs) | Apply func(self, args, *kwargs). |
DataFrame.agg(self, func[, axis]) | Aggregate using one or more operations over the specified axis. |
DataFrame.aggregate(self, func[, axis]) | Aggregate using one or more operations over the specified axis. |
DataFrame.transform(self, func[, axis]) | Call func on self producing a DataFrame with transformed values and that has the same axis length as self. |
DataFrame.groupby(self[, by, axis, level, …]) | Group DataFrame or Series using a mapper or by a Series of columns. |
DataFrame.rolling(self, window[, …]) | Provide rolling window calculations. |
DataFrame.expanding(self[, min_periods, …]) | Provide expanding transformations. |
DataFrame.ewm(self[, com, span, halflife, …]) | Provide exponential weighted functions. |
Computations / descriptive stats
DataFrame.abs(self) | Return a Series/DataFrame with absolute numeric value of each element. |
DataFrame.all(self[, axis, bool_only, …]) | Return whether all elements are True, potentially over an axis. |
DataFrame.any(self[, axis, bool_only, …]) | Return whether any element is True, potentially over an axis. |
DataFrame.clip(self[, lower, upper, axis, …]) | Trim values at input threshold(s). |
DataFrame.clip_lower(self, threshold[, …]) | (DEPRECATED) Trim values below a given threshold. |
DataFrame.clip_upper(self, threshold[, …]) | (DEPRECATED) Trim values above a given threshold. |
DataFrame.compound(self[, axis, skipna, level]) | (DEPRECATED) Return the compound percentage of the values for the requested axis. |
DataFrame.corr(self[, method, min_periods]) | Compute pairwise correlation of columns, excluding NA/null values. |
DataFrame.corrwith(self, other[, axis, …]) | Compute pairwise correlation between rows or columns of DataFrame with rows or columns of Series or DataFrame. |
DataFrame.count(self[, axis, level, …]) | Count non-NA cells for each column or row. |
DataFrame.cov(self[, min_periods]) | Compute pairwise covariance of columns, excluding NA/null values. |
DataFrame.cummax(self[, axis, skipna]) | Return cumulative maximum over a DataFrame or Series axis. |
DataFrame.cummin(self[, axis, skipna]) | Return cumulative minimum over a DataFrame or Series axis. |
DataFrame.cumprod(self[, axis, skipna]) | Return cumulative product over a DataFrame or Series axis. |
DataFrame.cumsum(self[, axis, skipna]) | Return cumulative sum over a DataFrame or Series axis. |
DataFrame.describe(self[, percentiles, …]) | Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. |
DataFrame.diff(self[, periods, axis]) | First discrete difference of element. |
DataFrame.eval(self, expr[, inplace]) | Evaluate a string describing operations on DataFrame columns. |
DataFrame.kurt(self[, axis, skipna, level, …]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
DataFrame.kurtosis(self[, axis, skipna, …]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
DataFrame.mad(self[, axis, skipna, level]) | Return the mean absolute deviation of the values for the requested axis. |
DataFrame.max(self[, axis, skipna, level, …]) | Return the maximum of the values for the requested axis. |
DataFrame.mean(self[, axis, skipna, level, …]) | Return the mean of the values for the requested axis. |
DataFrame.median(self[, axis, skipna, …]) | Return the median of the values for the requested axis. |
DataFrame.min(self[, axis, skipna, level, …]) | Return the minimum of the values for the requested axis. |
DataFrame.mode(self[, axis, numeric_only, …]) | Get the mode(s) of each element along the selected axis. |
DataFrame.pct_change(self[, periods, …]) | Percentage change between the current and a prior element. |
DataFrame.prod(self[, axis, skipna, level, …]) | Return the product of the values for the requested axis. |
DataFrame.product(self[, axis, skipna, …]) | Return the product of the values for the requested axis. |
DataFrame.quantile(self[, q, axis, …]) | Return values at the given quantile over requested axis. |
DataFrame.rank(self[, axis, method, …]) | Compute numerical data ranks (1 through n) along axis. |
DataFrame.round(self[, decimals]) | Round a DataFrame to a variable number of decimal places. |
DataFrame.sem(self[, axis, skipna, level, …]) | Return unbiased standard error of the mean over requested axis. |
DataFrame.skew(self[, axis, skipna, level, …]) | Return unbiased skew over requested axis Normalized by N-1. |
DataFrame.sum(self[, axis, skipna, level, …]) | Return the sum of the values for the requested axis. |
DataFrame.std(self[, axis, skipna, level, …]) | Return sample standard deviation over requested axis. |
DataFrame.var(self[, axis, skipna, level, …]) | Return unbiased variance over requested axis. |
DataFrame.nunique(self[, axis, dropna]) | Count distinct observations over requested axis. |
Reindexing / selection / label manipulation
DataFrame.add_prefix(self, prefix) | Prefix labels with string prefix. |
DataFrame.add_suffix(self, suffix) | Suffix labels with string suffix. |
DataFrame.align(self, other[, join, axis, …]) | Align two objects on their axes with the specified join method for each axis Index. |
DataFrame.at_time(self, time[, asof, axis]) | Select values at particular time of day (e.g. |
DataFrame.between_time(self, starttime, …) | Select values between particular times of the day (e.g., 9:00-9:30 AM). |
DataFrame.drop(self[, labels, axis, index, …]) | Drop specified labels from rows or columns. |
DataFrame.drop_duplicates(self[, subset, …]) | Return DataFrame with duplicate rows removed, optionally only considering certain columns. |
DataFrame.duplicated(self[, subset, keep]) | Return boolean Series denoting duplicate rows, optionally only considering certain columns. |
DataFrame.equals(self, other) | Test whether two objects contain the same elements. |
DataFrame.filter(self[, items, like, regex, …]) | Subset rows or columns of dataframe according to labels in the specified index. |
DataFrame.first(self, offset) | Convenience method for subsetting initial periods of time series data based on a date offset. |
DataFrame.head(self[, n]) | Return the first _n rows. |
DataFrame.idxmax(self[, axis, skipna]) | Return index of first occurrence of maximum over requested axis. |
DataFrame.idxmin(self[, axis, skipna]) | Return index of first occurrence of minimum over requested axis. |
DataFrame.last(self, offset) | Convenience method for subsetting final periods of time series data based on a date offset. |
DataFrame.reindex(self[, labels, index, …]) | Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. |
DataFrame.reindex_like(self, other[, …]) | Return an object with matching indices as other object. |
DataFrame.rename(self[, mapper, index, …]) | Alter axes labels. |
DataFrame.rename_axis(self[, mapper, index, …]) | Set the name of the axis for the index or columns. |
DataFrame.reset_index(self[, level, drop, …]) | Reset the index, or a level of it. |
DataFrame.sample(self[, n, frac, replace, …]) | Return a random sample of items from an axis of object. |
DataFrame.set_axis(self, labels[, axis, inplace]) | Assign desired index to given axis. |
DataFrame.set_index(self, keys[, drop, …]) | Set the DataFrame index using existing columns. |
DataFrame.tail(self[, n]) | Return the last n rows. |
DataFrame.take(self, indices[, axis, iscopy]) | Return the elements in the given _positional indices along an axis. |
DataFrame.truncate(self[, before, after, …]) | Truncate a Series or DataFrame before and after some index value. |
Missing data handling
DataFrame.dropna(self[, axis, how, thresh, …]) | Remove missing values. |
DataFrame.fillna(self[, value, method, …]) | Fill NA/NaN values using the specified method. |
DataFrame.replace(self[, toreplace, value, …]) | Replace values given in _to_replace with value. |
DataFrame.interpolate(self[, method, axis, …]) | Interpolate values according to different methods. |
Reshaping, sorting, transposing
DataFrame.droplevel(self, level[, axis]) | Return DataFrame with requested index / column level(s) removed. |
DataFrame.pivot(self[, index, columns, values]) | Return reshaped DataFrame organized by given index / column values. |
DataFrame.pivot_table(self[, values, index, …]) | Create a spreadsheet-style pivot table as a DataFrame. |
DataFrame.reorder_levels(self, order[, axis]) | Rearrange index levels using input order. |
DataFrame.sort_values(self, by[, axis, …]) | Sort by the values along either axis. |
DataFrame.sort_index(self[, axis, level, …]) | Sort object by labels (along an axis). |
DataFrame.nlargest(self, n, columns[, keep]) | Return the first n rows ordered by columns in descending order. |
DataFrame.nsmallest(self, n, columns[, keep]) | Return the first n rows ordered by columns in ascending order. |
DataFrame.swaplevel(self[, i, j, axis]) | Swap levels i and j in a MultiIndex on a particular axis. |
DataFrame.stack(self[, level, dropna]) | Stack the prescribed level(s) from columns to index. |
DataFrame.unstack(self[, 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(self, axis1, axis2[, copy]) | Interchange axes and swap values axes appropriately. |
DataFrame.melt(self[, id_vars, value_vars, …]) | Unpivot a DataFrame from wide format to long format, optionally leaving identifier variables set. |
DataFrame.explode(self, column, Tuple]) | Transform each element of a list-like to a row, replicating the index values. |
DataFrame.squeeze(self[, axis]) | Squeeze 1 dimensional axis objects into scalars. |
DataFrame.to_xarray(self) | Return an xarray object from the pandas object. |
DataFrame.T | Transpose index and columns. |
DataFrame.transpose(self, *args, **kwargs) | Transpose index and columns. |
Combining / joining / merging
DataFrame.append(self, other[, …]) | Append rows of other to the end of caller, returning a new object. |
DataFrame.assign(self, **kwargs) | Assign new columns to a DataFrame. |
DataFrame.join(self, other[, on, how, …]) | Join columns of another DataFrame. |
DataFrame.merge(self, right[, how, on, …]) | Merge DataFrame or named Series objects with a database-style join. |
DataFrame.update(self, other[, join, …]) | Modify in place using non-NA values from another DataFrame. |
Time series-related
DataFrame.asfreq(self, freq[, method, how, …]) | Convert TimeSeries to specified frequency. |
DataFrame.asof(self, where[, subset]) | Return the last row(s) without any NaNs before where. |
DataFrame.shift(self[, periods, freq, axis, …]) | Shift index by desired number of periods with an optional time freq. |
DataFrame.slice_shift(self[, periods, axis]) | Equivalent to shift without copying data. |
DataFrame.tshift(self[, periods, freq, axis]) | Shift the time index, using the index’s frequency if available. |
DataFrame.first_valid_index(self) | Return index for first non-NA/null value. |
DataFrame.last_valid_index(self) | Return index for last non-NA/null value. |
DataFrame.resample(self, rule[, how, axis, …]) | Resample time-series data. |
DataFrame.to_period(self[, freq, axis, copy]) | Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). |
DataFrame.to_timestamp(self[, freq, how, …]) | Cast to DatetimeIndex of timestamps, at beginning of period. |
DataFrame.tz_convert(self, tz[, axis, …]) | Convert tz-aware axis to target time zone. |
DataFrame.tz_localize(self, tz[, axis, …]) | 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 forspecific plotting methods of the form DataFrame.plot.<kind>.
DataFrame.plot([x, y, kind, ax, ….]) | DataFrame plotting accessor and method |
DataFrame.plot.area(self[, x, y]) | Draw a stacked area plot. |
DataFrame.plot.bar(self[, x, y]) | Vertical bar plot. |
DataFrame.plot.barh(self[, x, y]) | Make a horizontal bar plot. |
DataFrame.plot.box(self[, by]) | Make a box plot of the DataFrame columns. |
DataFrame.plot.density(self[, bw_method, ind]) | Generate Kernel Density Estimate plot using Gaussian kernels. |
DataFrame.plot.hexbin(self, x, y[, C, …]) | Generate a hexagonal binning plot. |
DataFrame.plot.hist(self[, by, bins]) | Draw one histogram of the DataFrame’s columns. |
DataFrame.plot.kde(self[, bw_method, ind]) | Generate Kernel Density Estimate plot using Gaussian kernels. |
DataFrame.plot.line(self[, x, y]) | Plot Series or DataFrame as lines. |
DataFrame.plot.pie(self, **kwargs) | Generate a pie plot. |
DataFrame.plot.scatter(self, x, y[, s, c]) | Create a scatter plot with varying marker point size and color. |
DataFrame.boxplot(self[, column, by, ax, …]) | Make a box plot from DataFrame columns. |
DataFrame.hist(data[, column, by, grid, …]) | Make a histogram of the DataFrame’s. |
Sparse accessor
Sparse-dtype specific methods and attributes are provided under theDataFrame.sparse accessor.
DataFrame.sparse.density | Ratio of non-sparse points to total (dense) data points represented in the DataFrame. |
DataFrame.sparse.from_spmatrix(data[, …]) | Create a new DataFrame from a scipy sparse matrix. |
DataFrame.sparse.to_coo(self) | Return the contents of the frame as a sparse SciPy COO matrix. |
DataFrame.sparse.to_dense(self) | Convert a DataFrame with sparse values to dense. |
Serialization / IO / conversion
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(self[, verbose, buf, …]) | Print a concise summary of a DataFrame. |
DataFrame.to_parquet(self, fname[, engine, …]) | Write a DataFrame to the binary parquet format. |
DataFrame.to_pickle(self, path[, …]) | Pickle (serialize) object to file. |
DataFrame.to_csv(self[, path_or_buf, sep, …]) | Write object to a comma-separated values (csv) file. |
DataFrame.to_hdf(self, path_or_buf, key, …) | Write the contained data to an HDF5 file using HDFStore. |
DataFrame.to_sql(self, name, con[, schema, …]) | Write records stored in a DataFrame to a SQL database. |
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_json(self[, path_or_buf, …]) | Convert the object to a JSON string. |
DataFrame.to_html(self[, buf, columns, …]) | Render a DataFrame as an HTML table. |
DataFrame.to_feather(self, fname) | Write out the binary feather-format for DataFrames. |
DataFrame.to_latex(self[, buf, columns, …]) | Render an object to a LaTeX tabular environment table. |
DataFrame.to_stata(self, fname[, …]) | Export DataFrame object to Stata dta format. |
DataFrame.to_msgpack(self[, path_or_buf, …]) | (DEPRECATED) Serialize object to input file path using msgpack format. |
DataFrame.to_gbq(self, destination_table[, …]) | Write a DataFrame to a Google BigQuery table. |
DataFrame.to_records(self[, index, …]) | Convert DataFrame to a NumPy record array. |
DataFrame.to_sparse(self[, fill_value, kind]) | (DEPRECATED) Convert to SparseDataFrame. |
DataFrame.to_dense(self) | (DEPRECATED) Return dense representation of Series/DataFrame (as opposed to sparse). |
DataFrame.to_string(self[, buf, columns, …]) | Render a DataFrame to a console-friendly tabular output. |
DataFrame.to_clipboard(self[, 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(self) | Return the contents of the frame as a sparse SciPy COO matrix. |
