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. |
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. |
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. |
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. |
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. |
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. |
DataFrame.asfreq(freq[, method, how, …]) | Convert TimeSeries to specified frequency. |
DataFrame.asof(where[, subset]) | Return the last row(s) without any NaNs before where. |
DataFrame.shift([periods, freq, axis, …]) | Shift index by desired number of periods with an optional time freq. |
DataFrame.slice_shift([periods, axis]) | Equivalent to shift without copying data. |
DataFrame.tshift([periods, freq, axis]) | Shift the time index, using the index’s frequency if available. |
DataFrame.first_valid_index() | Return index for first non-NA/null value. |
DataFrame.last_valid_index() | Return index for last non-NA/null value. |
DataFrame.resample(rule[, how, axis, …]) | Resample time-series data. |
DataFrame.to_period([freq, axis, copy]) | Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed). |
DataFrame.to_timestamp([freq, how, axis, copy]) | Cast to DatetimeIndex of timestamps, at beginning 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. |
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. |