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]) |
|
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.