This page gives an overview of all public pandas objects, functions and methods. All classes and functions exposed in pandas.*
namespace are public.
melt (frame[, id_vars, value_vars, var_name, …]) | “Unpivots” a DataFrame from wide format to long format, optionally leaving identifier variables set. |
pivot (index, columns, values) | Produce ‘pivot’ table based on 3 columns of this DataFrame. |
pivot_table (data[, values, index, columns, …]) | Create a spreadsheet-style pivot table as a DataFrame. |
crosstab (index, columns[, values, rownames, …]) | Compute a simple cross-tabulation of two (or more) factors. |
cut (x, bins[, right, labels, retbins, …]) | Bin values into discrete intervals. |
qcut (x, q[, labels, retbins, precision, …]) | Quantile-based discretization function. |
merge (left, right[, how, on, left_on, …]) | Merge DataFrame objects by performing a database-style join operation by columns or indexes. |
merge_ordered (left, right[, on, left_on, …]) | Perform merge with optional filling/interpolation designed for ordered data like time series data. |
merge_asof (left, right[, on, left_on, …]) | Perform an asof merge. |
concat (objs[, axis, join, join_axes, …]) | Concatenate pandas objects along a particular axis with optional set logic along the other axes. |
get_dummies (data[, prefix, prefix_sep, …]) | Convert categorical variable into dummy/indicator variables |
factorize (values[, sort, order, …]) | Encode the object as an enumerated type or categorical variable. |
unique (values) | Hash table-based unique. |
wide_to_long (df, stubnames, i, j[, sep, suffix]) | Wide panel to long format. |
to_datetime (arg[, errors, dayfirst, …]) | Convert argument to datetime. |
to_timedelta (arg[, unit, box, errors]) | Convert argument to timedelta |
date_range ([start, end, periods, freq, tz, …]) | Return a fixed frequency DatetimeIndex. |
bdate_range ([start, end, periods, freq, tz, …]) | Return a fixed frequency DatetimeIndex, with business day as the default frequency |
period_range ([start, end, periods, freq, name]) | Return a fixed frequency PeriodIndex, with day (calendar) as the default frequency |
timedelta_range ([start, end, periods, freq, …]) | Return a fixed frequency TimedeltaIndex, with day as the default frequency |
infer_freq (index[, warn]) | Infer the most likely frequency given the input index. |
Series.add (other[, level, fill_value, axis]) | Addition of series and other, element-wise (binary operator add ). |
Series.sub (other[, level, fill_value, axis]) | Subtraction of series and other, element-wise (binary operator sub ). |
Series.mul (other[, level, fill_value, axis]) | Multiplication of series and other, element-wise (binary operator mul ). |
Series.div (other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operator truediv ). |
Series.truediv (other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operator truediv ). |
Series.floordiv (other[, level, fill_value, axis]) | Integer division of series and other, element-wise (binary operator floordiv ). |
Series.mod (other[, level, fill_value, axis]) | Modulo of series and other, element-wise (binary operator mod ). |
Series.pow (other[, level, fill_value, axis]) | Exponential power of series and other, element-wise (binary operator pow ). |
Series.radd (other[, level, fill_value, axis]) | Addition of series and other, element-wise (binary operator radd ). |
Series.rsub (other[, level, fill_value, axis]) | Subtraction of series and other, element-wise (binary operator rsub ). |
Series.rmul (other[, level, fill_value, axis]) | Multiplication of series and other, element-wise (binary operator rmul ). |
Series.rdiv (other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operator rtruediv ). |
Series.rtruediv (other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operator rtruediv ). |
Series.rfloordiv (other[, level, fill_value, …]) | Integer division of series and other, element-wise (binary operator rfloordiv ). |
Series.rmod (other[, level, fill_value, axis]) | Modulo of series and other, element-wise (binary operator rmod ). |
Series.rpow (other[, level, fill_value, axis]) | Exponential power of series and other, element-wise (binary operator rpow ). |
Series.combine (other, func[, fill_value]) | Perform elementwise binary operation on two Series using given function with optional fill value when an index is missing from one Series or the other |
Series.combine_first (other) | Combine Series values, choosing the calling Series’s values first. |
Series.round ([decimals]) | Round each value in a Series to the given number of decimals. |
Series.lt (other[, level, fill_value, axis]) | Less than of series and other, element-wise (binary operator lt ). |
Series.gt (other[, level, fill_value, axis]) | Greater than of series and other, element-wise (binary operator gt ). |
Series.le (other[, level, fill_value, axis]) | Less than or equal to of series and other, element-wise (binary operator le ). |
Series.ge (other[, level, fill_value, axis]) | Greater than or equal to of series and other, element-wise (binary operator ge ). |
Series.ne (other[, level, fill_value, axis]) | Not equal to of series and other, element-wise (binary operator ne ). |
Series.eq (other[, level, fill_value, axis]) | Equal to of series and other, element-wise (binary operator eq ). |
Series.product ([axis, skipna, level, …]) | Return the product of the values for the requested axis |
Series.dot (other) | Matrix multiplication with DataFrame or inner-product with Series objects. |
Series.apply (func[, convert_dtype, args]) | Invoke function on values of Series. |
Series.agg (func[, axis]) | Aggregate using one or more operations over the specified axis. |
Series.aggregate (func[, axis]) | Aggregate using one or more operations over the specified axis. |
Series.transform (func, *args, **kwargs) | Call function producing a like-indexed NDFrame and return a NDFrame with the transformed values |
Series.map (arg[, na_action]) | Map values of Series using input correspondence (a dict, Series, or function). |
Series.groupby ([by, axis, level, as_index, …]) | Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns. |
Series.rolling (window[, min_periods, …]) | Provides rolling window calculations. |
Series.expanding ([min_periods, center, axis]) | Provides expanding transformations. |
Series.ewm ([com, span, halflife, alpha, …]) | Provides exponential weighted functions |
Series.pipe (func, *args, **kwargs) | Apply func(self, *args, **kwargs) |
Series.abs () | Return a Series/DataFrame with absolute numeric value of each element. |
Series.all ([axis, bool_only, skipna, level]) | Return whether all elements are True, potentially over an axis. |
Series.any ([axis, bool_only, skipna, level]) | Return whether any element is True over requested axis. |
Series.autocorr ([lag]) | Lag-N autocorrelation |
Series.between (left, right[, inclusive]) | Return boolean Series equivalent to left <= series <= right. |
Series.clip ([lower, upper, axis, inplace]) | Trim values at input threshold(s). |
Series.clip_lower (threshold[, axis, inplace]) | Return copy of the input with values below a threshold truncated. |
Series.clip_upper (threshold[, axis, inplace]) | Return copy of input with values above given value(s) truncated. |
Series.corr (other[, method, min_periods]) | Compute correlation with other Series, excluding missing values |
Series.count ([level]) | Return number of non-NA/null observations in the Series |
Series.cov (other[, min_periods]) | Compute covariance with Series, excluding missing values |
Series.cummax ([axis, skipna]) | Return cumulative maximum over a DataFrame or Series axis. |
Series.cummin ([axis, skipna]) | Return cumulative minimum over a DataFrame or Series axis. |
Series.cumprod ([axis, skipna]) | Return cumulative product over a DataFrame or Series axis. |
Series.cumsum ([axis, skipna]) | Return cumulative sum over a DataFrame or Series axis. |
Series.describe ([percentiles, include, exclude]) | Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. |
Series.diff ([periods]) | First discrete difference of element. |
Series.factorize ([sort, na_sentinel]) | Encode the object as an enumerated type or categorical variable. |
Series.kurt ([axis, skipna, level, numeric_only]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
Series.mad ([axis, skipna, level]) | Return the mean absolute deviation of the values for the requested axis |
Series.max ([axis, skipna, level, numeric_only]) | This method returns the maximum of the values in the object. |
Series.mean ([axis, skipna, level, numeric_only]) | Return the mean of the values for the requested axis |
Series.median ([axis, skipna, level, …]) | Return the median of the values for the requested axis |
Series.min ([axis, skipna, level, numeric_only]) | This method returns the minimum of the values in the object. |
Series.mode () | Return the mode(s) of the dataset. |
Series.nlargest ([n, keep]) | Return the largest n elements. |
Series.nsmallest ([n, keep]) | Return the smallest n elements. |
Series.pct_change ([periods, fill_method, …]) | Percentage change between the current and a prior element. |
Series.prod ([axis, skipna, level, …]) | Return the product of the values for the requested axis |
Series.quantile ([q, interpolation]) | Return value at the given quantile, a la numpy.percentile. |
Series.rank ([axis, method, numeric_only, …]) | Compute numerical data ranks (1 through n) along axis. |
Series.sem ([axis, skipna, level, ddof, …]) | Return unbiased standard error of the mean over requested axis. |
Series.skew ([axis, skipna, level, numeric_only]) | Return unbiased skew over requested axis Normalized by N-1 |
Series.std ([axis, skipna, level, ddof, …]) | Return sample standard deviation over requested axis. |
Series.sum ([axis, skipna, level, …]) | Return the sum of the values for the requested axis |
Series.var ([axis, skipna, level, ddof, …]) | Return unbiased variance over requested axis. |
Series.kurtosis ([axis, skipna, level, …]) | Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). |
Series.unique () | Return unique values of Series object. |
Series.nunique ([dropna]) | Return number of unique elements in the object. |
Series.is_unique | Return boolean if values in the object are unique |
Series.is_monotonic | Return boolean if values in the object are monotonic_increasing |
Series.is_monotonic_increasing | Return boolean if values in the object are monotonic_increasing |
Series.is_monotonic_decreasing | Return boolean if values in the object are monotonic_decreasing |
Series.value_counts ([normalize, sort, …]) | Returns object containing counts of unique values. |
Series.compound ([axis, skipna, level]) | Return the compound percentage of the values for the requested axis |
Series.nonzero () | Return the integer indices of the elements that are non-zero |
Series.ptp ([axis, skipna, level, numeric_only]) | Returns the difference between the maximum value and the |
Series.align (other[, join, axis, level, …]) | Align two objects on their axes with the specified join method for each axis Index |
Series.drop ([labels, axis, index, columns, …]) | Return Series with specified index labels removed. |
Series.drop_duplicates ([keep, inplace]) | Return Series with duplicate values removed. |
Series.duplicated ([keep]) | Indicate duplicate Series values. |
Series.equals (other) | Determines if two NDFrame objects contain the same elements. |
Series.first (offset) | Convenience method for subsetting initial periods of time series data based on a date offset. |
Series.head ([n]) | Return the first n rows. |
Series.idxmax ([axis, skipna]) | Return the row label of the maximum value. |
Series.idxmin ([axis, skipna]) | Return the row label of the minimum value. |
Series.isin (values) | Check whether values are contained in Series. |
Series.last (offset) | Convenience method for subsetting final periods of time series data based on a date offset. |
Series.reindex ([index]) | Conform Series to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. |
Series.reindex_like (other[, method, copy, …]) | Return an object with matching indices to myself. |
Series.rename ([index]) | Alter Series index labels or name |
Series.rename_axis (mapper[, axis, copy, inplace]) | Alter the name of the index or columns. |
Series.reset_index ([level, drop, name, inplace]) | Generate a new DataFrame or Series with the index reset. |
Series.sample ([n, frac, replace, weights, …]) | Return a random sample of items from an axis of object. |
Series.select (crit[, axis]) | (DEPRECATED) Return data corresponding to axis labels matching criteria |
Series.set_axis (labels[, axis, inplace]) | Assign desired index to given axis. |
Series.take (indices[, axis, convert, is_copy]) | Return the elements in the given positional indices along an axis. |
Series.tail ([n]) | Return the last n rows. |
Series.truncate ([before, after, axis, copy]) | Truncate a Series or DataFrame before and after some index value. |
Series.where (cond[, other, inplace, axis, …]) | Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other . |
Series.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 . |
Series.add_prefix (prefix) | Prefix labels with string prefix . |
Series.add_suffix (suffix) | Suffix labels with string suffix . |
Series.filter ([items, like, regex, axis]) | Subset rows or columns of dataframe according to labels in the specified index. |
Series.argsort ([axis, kind, order]) | Overrides ndarray.argsort. |
Series.argmin ([axis, skipna]) | (DEPRECATED) .. deprecated:: 0.21.0 |
Series.argmax ([axis, skipna]) | (DEPRECATED) .. deprecated:: 0.21.0 |
Series.reorder_levels (order) | Rearrange index levels using input order. |
Series.sort_values ([axis, ascending, …]) | Sort by the values. |
Series.sort_index ([axis, level, ascending, …]) | Sort Series by index labels. |
Series.swaplevel ([i, j, copy]) | Swap levels i and j in a MultiIndex |
Series.unstack ([level, fill_value]) | Unstack, a.k.a. |
Series.searchsorted (value[, side, sorter]) | Find indices where elements should be inserted to maintain order. |
Series.ravel ([order]) | Return the flattened underlying data as an ndarray |
Series.repeat (repeats, *args, **kwargs) | Repeat elements of an Series. |
Series.squeeze ([axis]) | Squeeze length 1 dimensions. |
Series.view ([dtype]) | Create a new view of the Series. |
Series.sortlevel ([level, ascending, …]) | (DEPRECATED) Sort Series with MultiIndex by chosen level. |
Series.asfreq (freq[, method, how, …]) | Convert TimeSeries to specified frequency. |
Series.asof (where[, subset]) | The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) |
Series.shift ([periods, freq, axis]) | Shift index by desired number of periods with an optional time freq |
Series.first_valid_index () | Return index for first non-NA/null value. |
Series.last_valid_index () | Return index for last non-NA/null value. |
Series.resample (rule[, how, axis, …]) | Convenience method for frequency conversion and resampling of time series. |
Series.tz_convert (tz[, axis, level, copy]) | Convert tz-aware axis to target time zone. |
Series.tz_localize (tz[, axis, level, copy, …]) | Localize tz-naive TimeSeries to target time zone. |
Series.at_time (time[, asof]) | Select values at particular time of day (e.g. |
Series.between_time (start_time, end_time[, …]) | Select values between particular times of the day (e.g., 9:00-9:30 AM). |
Series.tshift ([periods, freq, axis]) | Shift the time index, using the index’s frequency if available. |
Series.slice_shift ([periods, axis]) | Equivalent to shift without copying data. |
Series.str.capitalize () | Convert strings in the Series/Index to be capitalized. |
Series.str.cat ([others, sep, na_rep, join]) | Concatenate strings in the Series/Index with given separator. |
Series.str.center (width[, fillchar]) | Filling left and right side of strings in the Series/Index with an additional character. |
Series.str.contains (pat[, case, flags, na, …]) | Test if pattern or regex is contained within a string of a Series or Index. |
Series.str.count (pat[, flags]) | Count occurrences of pattern in each string of the Series/Index. |
Series.str.decode (encoding[, errors]) | Decode character string in the Series/Index using indicated encoding. |
Series.str.encode (encoding[, errors]) | Encode character string in the Series/Index using indicated encoding. |
Series.str.endswith (pat[, na]) | Test if the end of each string element matches a pattern. |
Series.str.extract (pat[, flags, expand]) | For each subject string in the Series, extract groups from the first match of regular expression pat. |
Series.str.extractall (pat[, flags]) | For each subject string in the Series, extract groups from all matches of regular expression pat. |
Series.str.find (sub[, start, end]) | Return lowest indexes in each strings in the Series/Index where the substring is fully contained between [start:end]. |
Series.str.findall (pat[, flags]) | Find all occurrences of pattern or regular expression in the Series/Index. |
Series.str.get (i) | Extract element from each component at specified position. |
Series.str.index (sub[, start, end]) | Return lowest indexes in each strings where the substring is fully contained between [start:end]. |
Series.str.join (sep) | Join lists contained as elements in the Series/Index with passed delimiter. |
Series.str.len () | Compute length of each string in the Series/Index. |
Series.str.ljust (width[, fillchar]) | Filling right side of strings in the Series/Index with an additional character. |
Series.str.lower () | Convert strings in the Series/Index to lowercase. |
Series.str.lstrip ([to_strip]) | Strip whitespace (including newlines) from each string in the Series/Index from left side. |
Series.str.match (pat[, case, flags, na, …]) | Determine if each string matches a regular expression. |
Series.str.normalize (form) | Return the Unicode normal form for the strings in the Series/Index. |
Series.str.pad (width[, side, fillchar]) | Pad strings in the Series/Index with an additional character to specified side. |
Series.str.partition ([pat, expand]) | Split the string at the first occurrence of sep , and return 3 elements containing the part before the separator, the separator itself, and the part after the separator. |
Series.str.repeat (repeats) | Duplicate each string in the Series/Index by indicated number of times. |
Series.str.replace (pat, repl[, n, case, …]) | Replace occurrences of pattern/regex in the Series/Index with some other string. |
Series.str.rfind (sub[, start, end]) | Return highest indexes in each strings in the Series/Index where the substring is fully contained between [start:end]. |
Series.str.rindex (sub[, start, end]) | Return highest indexes in each strings where the substring is fully contained between [start:end]. |
Series.str.rjust (width[, fillchar]) | Filling left side of strings in the Series/Index with an additional character. |
Series.str.rpartition ([pat, expand]) | Split the string at the last occurrence of sep , and return 3 elements containing the part before the separator, the separator itself, and the part after the separator. |
Series.str.rstrip ([to_strip]) | Strip whitespace (including newlines) from each string in the Series/Index from right side. |
Series.str.slice ([start, stop, step]) | Slice substrings from each element in the Series/Index |
Series.str.slice_replace ([start, stop, repl]) | Replace a positional slice of a string with another value. |
Series.str.split ([pat, n, expand]) | Split strings around given separator/delimiter. |
Series.str.rsplit ([pat, n, expand]) | Split each string in the Series/Index by the given delimiter string, starting at the end of the string and working to the front. |
Series.str.startswith (pat[, na]) | Test if the start of each string element matches a pattern. |
Series.str.strip ([to_strip]) | Strip whitespace (including newlines) from each string in the Series/Index from left and right sides. |
Series.str.swapcase () | Convert strings in the Series/Index to be swapcased. |
Series.str.title () | Convert strings in the Series/Index to titlecase. |
Series.str.translate (table[, deletechars]) | Map all characters in the string through the given mapping table. |
Series.str.upper () | Convert strings in the Series/Index to uppercase. |
Series.str.wrap (width, **kwargs) | Wrap long strings in the Series/Index to be formatted in paragraphs with length less than a given width. |
Series.str.zfill (width) | Filling left side of strings in the Series/Index with 0. |
Series.str.isalnum () | Check whether all characters in each string in the Series/Index are alphanumeric. |
Series.str.isalpha () | Check whether all characters in each string in the Series/Index are alphabetic. |
Series.str.isdigit () | Check whether all characters in each string in the Series/Index are digits. |
Series.str.isspace () | Check whether all characters in each string in the Series/Index are whitespace. |
Series.str.islower () | Check whether all characters in each string in the Series/Index are lowercase. |
Series.str.isupper () | Check whether all characters in each string in the Series/Index are uppercase. |
Series.str.istitle () | Check whether all characters in each string in the Series/Index are titlecase. |
Series.str.isnumeric () | Check whether all characters in each string in the Series/Index are numeric. |
Series.str.isdecimal () | Check whether all characters in each string in the Series/Index are decimal. |
Series.str.get_dummies ([sep]) | Split each string in the Series by sep and return a frame of dummy/indicator variables. |
Pandas defines a custom data type for representing data that can take only a limited, fixed set of values. The dtype of a Categorical
can be described by a pandas.api.types.CategoricalDtype
.
Series.to_pickle (path[, compression, protocol]) | Pickle (serialize) object to file. |
Series.to_csv ([path, index, sep, na_rep, …]) | Write Series to a comma-separated values (csv) file |
Series.to_dict ([into]) | Convert Series to {label -> value} dict or dict-like object. |
Series.to_excel (excel_writer[, sheet_name, …]) | Write Series to an excel sheet |
Series.to_frame ([name]) | Convert Series to DataFrame |
Series.to_xarray () | Return an xarray object from the pandas object. |
Series.to_hdf (path_or_buf, key, **kwargs) | Write the contained data to an HDF5 file using HDFStore. |
Series.to_sql (name, con[, schema, …]) | Write records stored in a DataFrame to a SQL database. |
Series.to_msgpack ([path_or_buf, encoding]) | msgpack (serialize) object to input file path |
Series.to_json ([path_or_buf, orient, …]) | Convert the object to a JSON string. |
Series.to_sparse ([kind, fill_value]) | Convert Series to SparseSeries |
Series.to_dense () | Return dense representation of NDFrame (as opposed to sparse) |
Series.to_string ([buf, na_rep, …]) | Render a string representation of the Series |
Series.to_clipboard ([excel, sep]) | Copy object to the system clipboard. |
Series.to_latex ([buf, columns, col_space, …]) | Render an object to a tabular environment table. |
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.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, 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]) | Return the last n rows. |
DataFrame.xs (key[, axis, level, drop_level]) | Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. |
DataFrame.get (key[, default]) | Get item from object for given key (DataFrame column, Panel slice, etc.). |
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. |
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) | Matrix multiplication with DataFrame or Series objects. |
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 (column, time) one frame is missing a value, it will default to the other frame’s value (which might be NaN as well) |
DataFrame.combine_first (other) | Combine two DataFrame objects and default to non-null values in frame calling the method. |
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, *args, **kwargs) | Call function producing a like-indexed NDFrame and return a NDFrame with the transformed values |
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 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 over requested axis. |
DataFrame.clip ([lower, upper, axis, inplace]) | Trim values at input threshold(s). |
DataFrame.clip_lower (threshold[, axis, inplace]) | Return copy of the input with values below a threshold truncated. |
DataFrame.clip_upper (threshold[, axis, inplace]) | Return copy of input with values above given value(s) truncated. |
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]) | Compute pairwise correlation between rows or columns of two DataFrame objects. |
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, …]) | Generates 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, …]) | 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, …]) | 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, 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 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]) | Return Series with number of 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]) | 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) | 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]) | 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, …]) | 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 ([mapper, index, columns, …]) | Alter axes labels. |
DataFrame.rename_axis (mapper[, axis, copy, …]) | Alter the name of the index or columns. |
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, …]) | 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 (row labels) using one or more 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.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]) | 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]) | 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 length 1 dimensions. |
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]) | The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame) |
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, …]) | Convenience method for frequency conversion and resampling of time series. |
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 TimeSeries 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 DataFrame 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 DataFrame 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 tabular environment table. |
DataFrame.to_stata (fname[, convert_dates, …]) | Export Stata binary dta files. |
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 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. |
Panel.add (other[, axis]) | Addition of series and other, element-wise (binary operator add ). |
Panel.sub (other[, axis]) | Subtraction of series and other, element-wise (binary operator sub ). |
Panel.mul (other[, axis]) | Multiplication of series and other, element-wise (binary operator mul ). |
Panel.div (other[, axis]) | Floating division of series and other, element-wise (binary operator truediv ). |
Panel.truediv (other[, axis]) | Floating division of series and other, element-wise (binary operator truediv ). |
Panel.floordiv (other[, axis]) | Integer division of series and other, element-wise (binary operator floordiv ). |
Panel.mod (other[, axis]) | Modulo of series and other, element-wise (binary operator mod ). |
Panel.pow (other[, axis]) | Exponential power of series and other, element-wise (binary operator pow ). |
Panel.radd (other[, axis]) | Addition of series and other, element-wise (binary operator radd ). |
Panel.rsub (other[, axis]) | Subtraction of series and other, element-wise (binary operator rsub ). |
Panel.rmul (other[, axis]) | Multiplication of series and other, element-wise (binary operator rmul ). |
Panel.rdiv (other[, axis]) | Floating division of series and other, element-wise (binary operator rtruediv ). |
Panel.rtruediv (other[, axis]) | Floating division of series and other, element-wise (binary operator rtruediv ). |
Panel.rfloordiv (other[, axis]) | Integer division of series and other, element-wise (binary operator rfloordiv ). |
Panel.rmod (other[, axis]) | Modulo of series and other, element-wise (binary operator rmod ). |
Panel.rpow (other[, axis]) | Exponential power of series and other, element-wise (binary operator rpow ). |
Panel.lt (other[, axis]) | Wrapper for comparison method lt |
Panel.gt (other[, axis]) | Wrapper for comparison method gt |
Panel.le (other[, axis]) | Wrapper for comparison method le |
Panel.ge (other[, axis]) | Wrapper for comparison method ge |
Panel.ne (other[, axis]) | Wrapper for comparison method ne |
Panel.eq (other[, axis]) | Wrapper for comparison method eq |
Panel.abs () | Return a Series/DataFrame with absolute numeric value of each element. |
Panel.clip ([lower, upper, axis, inplace]) | Trim values at input threshold(s). |
Panel.clip_lower (threshold[, axis, inplace]) | Return copy of the input with values below a threshold truncated. |
Panel.clip_upper (threshold[, axis, inplace]) | Return copy of input with values above given value(s) truncated. |
Panel.count ([axis]) | Return number of observations over requested axis. |
Panel.cummax ([axis, skipna]) | Return cumulative maximum over a DataFrame or Series axis. |
Panel.cummin ([axis, skipna]) | Return cumulative minimum over a DataFrame or Series axis. |
Panel.cumprod ([axis, skipna]) | Return cumulative product over a DataFrame or Series axis. |
Panel.cumsum ([axis, skipna]) | Return cumulative sum over a DataFrame or Series axis. |
Panel.max ([axis, skipna, level, numeric_only]) | This method returns the maximum of the values in the object. |
Panel.mean ([axis, skipna, level, numeric_only]) | Return the mean of the values for the requested axis |
Panel.median ([axis, skipna, level, numeric_only]) | Return the median of the values for the requested axis |
Panel.min ([axis, skipna, level, numeric_only]) | This method returns the minimum of the values in the object. |
Panel.pct_change ([periods, fill_method, …]) | Percentage change between the current and a prior element. |
Panel.prod ([axis, skipna, level, …]) | Return the product of the values for the requested axis |
Panel.sem ([axis, skipna, level, ddof, …]) | Return unbiased standard error of the mean over requested axis. |
Panel.skew ([axis, skipna, level, numeric_only]) | Return unbiased skew over requested axis Normalized by N-1 |
Panel.sum ([axis, skipna, level, …]) | Return the sum of the values for the requested axis |
Panel.std ([axis, skipna, level, ddof, …]) | Return sample standard deviation over requested axis. |
Panel.var ([axis, skipna, level, ddof, …]) | Return unbiased variance over requested axis. |
Panel.add_prefix (prefix) | Prefix labels with string prefix . |
Panel.add_suffix (suffix) | Suffix labels with string suffix . |
Panel.drop ([labels, axis, index, columns, …]) | |
Panel.equals (other) | Determines if two NDFrame objects contain the same elements. |
Panel.filter ([items, like, regex, axis]) | Subset rows or columns of dataframe according to labels in the specified index. |
Panel.first (offset) | Convenience method for subsetting initial periods of time series data based on a date offset. |
Panel.last (offset) | Convenience method for subsetting final periods of time series data based on a date offset. |
Panel.reindex (*args, **kwargs) | Conform Panel to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. |
Panel.reindex_axis (labels[, axis, method, …]) | Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. |
Panel.reindex_like (other[, method, copy, …]) | Return an object with matching indices to myself. |
Panel.rename ([items, major_axis, minor_axis]) | Alter axes input function or functions. |
Panel.sample ([n, frac, replace, weights, …]) | Return a random sample of items from an axis of object. |
Panel.select (crit[, axis]) | (DEPRECATED) Return data corresponding to axis labels matching criteria |
Panel.take (indices[, axis, convert, is_copy]) | Return the elements in the given positional indices along an axis. |
Panel.truncate ([before, after, axis, copy]) | Truncate a Series or DataFrame before and after some index value. |
Index.all (*args, **kwargs) | Return whether all elements are True. |
Index.any (*args, **kwargs) | Return whether any element is True. |
Index.argmin ([axis]) | return a ndarray of the minimum argument indexer |
Index.argmax ([axis]) | return a ndarray of the maximum argument indexer |
Index.copy ([name, deep, dtype]) | Make a copy of this object. |
Index.delete (loc) | Make new Index with passed location(-s) deleted |
Index.drop (labels[, errors]) | Make new Index with passed list of labels deleted |
Index.drop_duplicates ([keep]) | Return Index with duplicate values removed. |
Index.duplicated ([keep]) | Indicate duplicate index values. |
Index.equals (other) | Determines if two Index objects contain the same elements. |
Index.factorize ([sort, na_sentinel]) | Encode the object as an enumerated type or categorical variable. |
Index.identical (other) | Similar to equals, but check that other comparable attributes are also equal |
Index.insert (loc, item) | Make new Index inserting new item at location. |
Index.is_ (other) | More flexible, faster check like is but that works through views |
Index.is_boolean () | |
Index.is_categorical () | Check if the Index holds categorical data. |
Index.is_floating () | |
Index.is_integer () | |
Index.is_interval () | |
Index.is_lexsorted_for_tuple (tup) | |
Index.is_mixed () | |
Index.is_numeric () | |
Index.is_object () | |
Index.min () | Return the minimum value of the Index. |
Index.max () | Return the maximum value of the Index. |
Index.reindex (target[, method, level, …]) | Create index with target’s values (move/add/delete values as necessary) |
Index.rename (name[, inplace]) | Set new names on index. |
Index.repeat (repeats, *args, **kwargs) | Repeat elements of an Index. |
Index.where (cond[, other]) |
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Index.take (indices[, axis, allow_fill, …]) | return a new Index of the values selected by the indices |
Index.putmask (mask, value) | return a new Index of the values set with the mask |
Index.set_names (names[, level, inplace]) | Set new names on index. |
Index.unique ([level]) | Return unique values in the index. |
Index.nunique ([dropna]) | Return number of unique elements in the object. |
Index.value_counts ([normalize, sort, …]) | Returns object containing counts of unique values. |
Index.asof (label) | For a sorted index, return the most recent label up to and including the passed label. |
Index.asof_locs (where, mask) | where : array of timestamps mask : array of booleans where data is not NA |
Index.contains (key) | return a boolean if this key is IN the index |
Index.get_duplicates () | (DEPRECATED) Extract duplicated index elements. |
Index.get_indexer (target[, method, limit, …]) | Compute indexer and mask for new index given the current index. |
Index.get_indexer_for (target, **kwargs) | guaranteed return of an indexer even when non-unique This dispatches to get_indexer or get_indexer_nonunique as appropriate |
Index.get_indexer_non_unique (target) | Compute indexer and mask for new index given the current index. |
Index.get_level_values (level) | Return an Index of values for requested level, equal to the length of the index. |
Index.get_loc (key[, method, tolerance]) | Get integer location, slice or boolean mask for requested label. |
Index.get_slice_bound (label, side, kind) | Calculate slice bound that corresponds to given label. |
Index.get_value (series, key) | Fast lookup of value from 1-dimensional ndarray. |
Index.get_values () | Return Index data as an numpy.ndarray . |
Index.set_value (arr, key, value) | Fast lookup of value from 1-dimensional ndarray. |
Index.isin (values[, level]) | Return a boolean array where the index values are in values . |
Index.slice_indexer ([start, end, step, kind]) | For an ordered or unique index, compute the slice indexer for input labels and step. |
Index.slice_locs ([start, end, step, kind]) | Compute slice locations for input labels. |
IntervalIndex.from_arrays (left, right[, …]) | Construct from two arrays defining the left and right bounds. |
IntervalIndex.from_tuples (data[, closed, …]) | Construct an IntervalIndex from a list/array of tuples |
IntervalIndex.from_breaks (breaks[, closed, …]) | Construct an IntervalIndex from an array of splits |
IntervalIndex.contains (key) | Return a boolean indicating if the key is IN the index |
IntervalIndex.left | Return the left endpoints of each Interval in the IntervalIndex as an Index |
IntervalIndex.right | Return the right endpoints of each Interval in the IntervalIndex as an Index |
IntervalIndex.mid | Return the midpoint of each Interval in the IntervalIndex as an Index |
IntervalIndex.closed | Whether the intervals are closed on the left-side, right-side, both or neither |
IntervalIndex.length | Return an Index with entries denoting the length of each Interval in the IntervalIndex |
IntervalIndex.values | Return the IntervalIndex’s data as a numpy array of Interval objects (with dtype=’object’) |
IntervalIndex.is_non_overlapping_monotonic | Return True if the IntervalIndex is non-overlapping (no Intervals share points) and is either monotonic increasing or monotonic decreasing, else False |
IntervalIndex.get_loc (key[, method]) | Get integer location, slice or boolean mask for requested label. |
IntervalIndex.get_indexer (target[, method, …]) | Compute indexer and mask for new index given the current index. |
Timestamp.astimezone | Convert tz-aware Timestamp to another time zone. |
Timestamp.ceil | return a new Timestamp ceiled to this resolution |
Timestamp.combine (date, time) | date, time -> datetime with same date and time fields |
Timestamp.ctime | Return ctime() style string. |
Timestamp.date | Return date object with same year, month and day. |
Timestamp.day_name | Return the day name of the Timestamp with specified locale. |
Timestamp.dst | Return self.tzinfo.dst(self). |
Timestamp.floor | return a new Timestamp floored to this resolution |
Timestamp.freq | |
Timestamp.freqstr | |
Timestamp.fromordinal (ordinal[, freq, tz]) | passed an ordinal, translate and convert to a ts note: by definition there cannot be any tz info on the ordinal itself |
Timestamp.fromtimestamp (ts) | timestamp[, tz] -> tz’s local time from POSIX timestamp. |
Timestamp.isocalendar | Return a 3-tuple containing ISO year, week number, and weekday. |
Timestamp.isoformat | |
Timestamp.isoweekday | Return the day of the week represented by the date. |
Timestamp.month_name | Return the month name of the Timestamp with specified locale. |
Timestamp.normalize | Normalize Timestamp to midnight, preserving tz information. |
Timestamp.now ([tz]) | Returns new Timestamp object representing current time local to tz. |
Timestamp.replace | implements datetime.replace, handles nanoseconds |
Timestamp.round | Round the Timestamp to the specified resolution |
Timestamp.strftime | format -> strftime() style string. |
Timestamp.strptime | string, format -> new datetime parsed from a string (like time.strptime()). |
Timestamp.time | Return time object with same time but with tzinfo=None. |
Timestamp.timestamp | Return POSIX timestamp as float. |
Timestamp.timetuple | Return time tuple, compatible with time.localtime(). |
Timestamp.timetz | Return time object with same time and tzinfo. |
Timestamp.to_datetime64 | Returns a numpy.datetime64 object with ‘ns’ precision |
Timestamp.to_julian_date | Convert TimeStamp to a Julian Date. |
Timestamp.to_period | Return an period of which this timestamp is an observation. |
Timestamp.to_pydatetime | Convert a Timestamp object to a native Python datetime object. |
Timestamp.today (cls[, tz]) | Return the current time in the local timezone. |
Timestamp.toordinal | Return proleptic Gregorian ordinal. |
Timestamp.tz_convert | Convert tz-aware Timestamp to another time zone. |
Timestamp.tz_localize | Convert naive Timestamp to local time zone, or remove timezone from tz-aware Timestamp. |
Timestamp.tzname | Return self.tzinfo.tzname(self). |
Timestamp.utcfromtimestamp (ts) | Construct a naive UTC datetime from a POSIX timestamp. |
Timestamp.utcnow () | Return a new Timestamp representing UTC day and time. |
Timestamp.utcoffset | Return self.tzinfo.utcoffset(self). |
Timestamp.utctimetuple | Return UTC time tuple, compatible with time.localtime(). |
Timestamp.weekday | Return the day of the week represented by the date. |
Rolling.count () | The rolling count of any non-NaN observations inside the window. |
Rolling.sum (*args, **kwargs) | Calculate rolling sum of given DataFrame or Series. |
Rolling.mean (*args, **kwargs) | Calculate the rolling mean of the values. |
Rolling.median (**kwargs) | Calculate the rolling median. |
Rolling.var ([ddof]) | Calculate unbiased rolling variance. |
Rolling.std ([ddof]) | Calculate rolling standard deviation. |
Rolling.min (*args, **kwargs) | Calculate the rolling minimum. |
Rolling.max (*args, **kwargs) | rolling maximum |
Rolling.corr ([other, pairwise]) | rolling sample correlation |
Rolling.cov ([other, pairwise, ddof]) | rolling sample covariance |
Rolling.skew (**kwargs) | Unbiased rolling skewness |
Rolling.kurt (**kwargs) | Calculate unbiased rolling kurtosis. |
Rolling.apply (func[, raw, args, kwargs]) | rolling function apply |
Rolling.aggregate (arg, *args, **kwargs) | Aggregate using one or more operations over the specified axis. |
Rolling.quantile (quantile[, interpolation]) | rolling quantile. |
Window.mean (*args, **kwargs) | Calculate the window mean of the values. |
Window.sum (*args, **kwargs) | Calculate window sum of given DataFrame or Series. |
Expanding.count (**kwargs) | The expanding count of any non-NaN observations inside the window. |
Expanding.sum (*args, **kwargs) | Calculate expanding sum of given DataFrame or Series. |
Expanding.mean (*args, **kwargs) | Calculate the expanding mean of the values. |
Expanding.median (**kwargs) | Calculate the expanding median. |
Expanding.var ([ddof]) | Calculate unbiased expanding variance. |
Expanding.std ([ddof]) | Calculate expanding standard deviation. |
Expanding.min (*args, **kwargs) | Calculate the expanding minimum. |
Expanding.max (*args, **kwargs) | expanding maximum |
Expanding.corr ([other, pairwise]) | expanding sample correlation |
Expanding.cov ([other, pairwise, ddof]) | expanding sample covariance |
Expanding.skew (**kwargs) | Unbiased expanding skewness |
Expanding.kurt (**kwargs) | Calculate unbiased expanding kurtosis. |
Expanding.apply (func[, raw, args, kwargs]) | expanding function apply |
Expanding.aggregate (arg, *args, **kwargs) | Aggregate using one or more operations over the specified axis. |
Expanding.quantile (quantile[, interpolation]) | expanding quantile. |
GroupBy.all ([skipna]) | Returns True if all values in the group are truthful, else False |
GroupBy.any ([skipna]) | Returns True if any value in the group is truthful, else False |
GroupBy.bfill ([limit]) | Backward fill the values |
GroupBy.count () | Compute count of group, excluding missing values |
GroupBy.cumcount ([ascending]) | Number each item in each group from 0 to the length of that group - 1. |
GroupBy.ffill ([limit]) | Forward fill the values |
GroupBy.first (**kwargs) | Compute first of group values |
GroupBy.head ([n]) | Returns first n rows of each group. |
GroupBy.last (**kwargs) | Compute last of group values |
GroupBy.max (**kwargs) | Compute max of group values |
GroupBy.mean (*args, **kwargs) | Compute mean of groups, excluding missing values |
GroupBy.median (**kwargs) | Compute median of groups, excluding missing values |
GroupBy.min (**kwargs) | Compute min of group values |
GroupBy.ngroup ([ascending]) | Number each group from 0 to the number of groups - 1. |
GroupBy.nth (n[, dropna]) | Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. |
GroupBy.ohlc () | Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex |
GroupBy.prod (**kwargs) | Compute prod of group values |
GroupBy.rank ([method, ascending, na_option, …]) | Provides the rank of values within each group. |
GroupBy.pct_change ([periods, fill_method, …]) | Calcuate pct_change of each value to previous entry in group |
GroupBy.size () | Compute group sizes |
GroupBy.sem ([ddof]) | Compute standard error of the mean of groups, excluding missing values |
GroupBy.std ([ddof]) | Compute standard deviation of groups, excluding missing values |
GroupBy.sum (**kwargs) | Compute sum of group values |
GroupBy.var ([ddof]) | Compute variance of groups, excluding missing values |
GroupBy.tail ([n]) | Returns last n rows of each group |
DataFrameGroupBy.agg (arg, *args, **kwargs) | Aggregate using one or more operations over the specified axis. |
DataFrameGroupBy.all ([skipna]) | Returns True if all values in the group are truthful, else False |
DataFrameGroupBy.any ([skipna]) | Returns True if any value in the group is truthful, else False |
DataFrameGroupBy.bfill ([limit]) | Backward fill the values |
DataFrameGroupBy.corr | Compute pairwise correlation of columns, excluding NA/null values |
DataFrameGroupBy.count () | Compute count of group, excluding missing values |
DataFrameGroupBy.cov | Compute pairwise covariance of columns, excluding NA/null values. |
DataFrameGroupBy.cummax ([axis]) | Cumulative max for each group |
DataFrameGroupBy.cummin ([axis]) | Cumulative min for each group |
DataFrameGroupBy.cumprod ([axis]) | Cumulative product for each group |
DataFrameGroupBy.cumsum ([axis]) | Cumulative sum for each group |
DataFrameGroupBy.describe (**kwargs) | Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. |
DataFrameGroupBy.diff | First discrete difference of element. |
DataFrameGroupBy.ffill ([limit]) | Forward fill the values |
DataFrameGroupBy.fillna | Fill NA/NaN values using the specified method |
DataFrameGroupBy.filter (func[, dropna]) | Return a copy of a DataFrame excluding elements from groups that do not satisfy the boolean criterion specified by func. |
DataFrameGroupBy.hist | Make a histogram of the DataFrame’s. |
DataFrameGroupBy.idxmax | Return index of first occurrence of maximum over requested axis. |
DataFrameGroupBy.idxmin | Return index of first occurrence of minimum over requested axis. |
DataFrameGroupBy.mad | Return the mean absolute deviation of the values for the requested axis |
DataFrameGroupBy.pct_change ([periods, …]) | Calcuate pct_change of each value to previous entry in group |
DataFrameGroupBy.plot | Class implementing the .plot attribute for groupby objects |
DataFrameGroupBy.quantile | Return values at the given quantile over requested axis, a la numpy.percentile. |
DataFrameGroupBy.rank ([method, ascending, …]) | Provides the rank of values within each group. |
DataFrameGroupBy.resample (rule, *args, **kwargs) | Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended |
DataFrameGroupBy.shift ([periods, freq, axis]) | Shift each group by periods observations |
DataFrameGroupBy.size () | Compute group sizes |
DataFrameGroupBy.skew | Return unbiased skew over requested axis Normalized by N-1 |
DataFrameGroupBy.take | Return the elements in the given positional indices along an axis. |
DataFrameGroupBy.tshift | Shift the time index, using the index’s frequency if available. |
Resampler.count ([_method]) | Compute count of group, excluding missing values |
Resampler.nunique ([_method]) | Returns number of unique elements in the group |
Resampler.first ([_method]) | Compute first of group values |
Resampler.last ([_method]) | Compute last of group values |
Resampler.max ([_method]) | Compute max of group values |
Resampler.mean ([_method]) | Compute mean of groups, excluding missing values |
Resampler.median ([_method]) | Compute median of groups, excluding missing values |
Resampler.min ([_method]) | Compute min of group values |
Resampler.ohlc ([_method]) | Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex |
Resampler.prod ([_method, min_count]) | Compute prod of group values |
Resampler.size () | Compute group sizes |
Resampler.sem ([_method]) | Compute standard error of the mean of groups, excluding missing values |
Resampler.std ([ddof]) | Compute standard deviation of groups, excluding missing values |
Resampler.sum ([_method, min_count]) | Compute sum of group values |
Resampler.var ([ddof]) | Compute variance of groups, excluding missing values |
Styler.apply (func[, axis, subset]) | Apply a function column-wise, row-wise, or table-wase, updating the HTML representation with the result. |
Styler.applymap (func[, subset]) | Apply a function elementwise, updating the HTML representation with the result. |
Styler.where (cond, value[, other, subset]) | Apply a function elementwise, updating the HTML representation with a style which is selected in accordance with the return value of a function. |
Styler.format (formatter[, subset]) | Format the text display value of cells. |
Styler.set_precision (precision) | Set the precision used to render. |
Styler.set_table_styles (table_styles) | Set the table styles on a Styler. |
Styler.set_table_attributes (attributes) | Set the table attributes. |
Styler.set_caption (caption) | Set the caption on a Styler |
Styler.set_properties ([subset]) | Convenience method for setting one or more non-data dependent properties or each cell. |
Styler.set_uuid (uuid) | Set the uuid for a Styler. |
Styler.clear () | “Reset” the styler, removing any previously applied styles. |
These are primarily intended for library authors looking to extend pandas objects.