API Reference
This page gives an overview of all public pandas objects, functions and methods. All classes and functions exposed in pandas.* namespace are public.
Some subpackages are public which include pandas.errors, pandas.plotting, and pandas.testing. Public functions in pandas.io and pandas.tseries submodules are mentioned in the documentation. pandas.api.types subpackage holds some public functions related to data types in pandas.
Warning
The pandas.core, pandas.compat, and pandas.util top-level modules are PRIVATE. Stable functionality in such modules is not guaranteed.
Input/Output
read_pickle(path[, compression]) |
Load pickled pandas object (or any object) from file. |
Flat File
read_table(filepath_or_buffer[, sep, …]) |
Read general delimited file into DataFrame |
read_csv(filepath_or_buffer[, sep, …]) |
Read CSV (comma-separated) file into DataFrame |
read_fwf(filepath_or_buffer[, colspecs, widths]) |
Read a table of fixed-width formatted lines into DataFrame |
read_msgpack(path_or_buf[, encoding, iterator]) |
Load msgpack pandas object from the specified file path |
Clipboard
read_clipboard([sep]) |
Read text from clipboard and pass to read_table. |
Excel
read_excel(io[, sheet_name, header, names, …]) |
Read an Excel table into a pandas DataFrame |
ExcelFile.parse([sheet_name, header, names, …]) |
Parse specified sheet(s) into a DataFrame |
JSON
read_json([path_or_buf, orient, typ, dtype, …]) |
Convert a JSON string to pandas object |
json_normalize(data[, record_path, meta, …]) |
“Normalize” semi-structured JSON data into a flat table |
build_table_schema(data[, index, …]) |
Create a Table schema from data. |
HTML
read_html(io[, match, flavor, header, …]) |
Read HTML tables into a list of DataFrame objects. |
HDFStore: PyTables (HDF5)
read_hdf(path_or_buf[, key, mode]) |
Read from the store, close it if we opened it. |
HDFStore.put(key, value[, format, append]) |
Store object in HDFStore |
HDFStore.append(key, value[, format, …]) |
Append to Table in file. |
HDFStore.get(key) |
Retrieve pandas object stored in file |
HDFStore.select(key[, where, start, stop, …]) |
Retrieve pandas object stored in file, optionally based on where criteria |
HDFStore.info() |
print detailed information on the store |
HDFStore.keys() |
Return a (potentially unordered) list of the keys corresponding to the objects stored in the HDFStore. |
Feather
read_feather(path[, nthreads]) |
Load a feather-format object from the file path |
Parquet
read_parquet(path[, engine, columns]) |
Load a parquet object from the file path, returning a DataFrame. |
SAS
read_sas(filepath_or_buffer[, format, …]) |
Read SAS files stored as either XPORT or SAS7BDAT format files. |
SQL
read_sql_table(table_name, con[, schema, …]) |
Read SQL database table into a DataFrame. |
read_sql_query(sql, con[, index_col, …]) |
Read SQL query into a DataFrame. |
read_sql(sql, con[, index_col, …]) |
Read SQL query or database table into a DataFrame. |
Google BigQuery
read_gbq(query[, project_id, index_col, …]) |
Load data from Google BigQuery. |
STATA
read_stata(filepath_or_buffer[, …]) |
Read Stata file into DataFrame. |
General functions
Data manipulations
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. |
Top-level missing data
isna(obj) |
Detect missing values for an array-like object. |
isnull(obj) |
Detect missing values for an array-like object. |
notna(obj) |
Detect non-missing values for an array-like object. |
notnull(obj) |
Detect non-missing values for an array-like object. |
Top-level conversions
to_numeric(arg[, errors, downcast]) |
Convert argument to a numeric type. |
Top-level dealing with datetimelike
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. |
Top-level dealing with intervals
interval_range([start, end, periods, freq, …]) |
Return a fixed frequency IntervalIndex |
Top-level evaluation
eval(expr[, parser, engine, truediv, …]) |
Evaluate a Python expression as a string using various backends. |
Testing
Series
Constructor
Series([data, index, dtype, name, copy, …]) |
One-dimensional ndarray with axis labels (including time series). |
Attributes
Axes
Conversion
Series.astype(dtype[, copy, errors]) |
Cast a pandas object to a specified dtype dtype. |
Series.infer_objects() |
Attempt to infer better dtypes for object columns. |
Series.convert_objects([convert_dates, …]) |
(DEPRECATED) Attempt to infer better dtype for object columns. |
Series.copy([deep]) |
Make a copy of this object’s indices and data. |
Series.bool() |
Return the bool of a single element PandasObject. |
Series.to_period([freq, copy]) |
Convert Series from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed) |
Series.to_timestamp([freq, how, copy]) |
Cast to datetimeindex of timestamps, at beginning of period |
Series.tolist() |
Return a list of the values. |
Series.get_values() |
same as values (but handles sparseness conversions); is a view |
Indexing, iteration
Series.get(key[, default]) |
Get item from object for given key (DataFrame column, Panel slice, etc.). |
Series.at |
Access a single value for a row/column label pair. |
Series.iat |
Access a single value for a row/column pair by integer position. |
Series.loc |
Access a group of rows and columns by label(s) or a boolean array. |
Series.iloc |
Purely integer-location based indexing for selection by position. |
Series.__iter__() |
Return an iterator of the values. |
Series.iteritems() |
Lazily iterate over (index, value) tuples |
Series.items() |
Lazily iterate over (index, value) tuples |
Series.keys() |
Alias for index |
Series.pop(item) |
Return item and drop from frame. |
Series.item() |
return the first element of the underlying data as a python scalar |
Series.xs(key[, axis, level, drop_level]) |
Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. |
For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.
Binary operator functions
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. |
Function application, GroupBy & Window
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) |
Computations / Descriptive Stats
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 |
Reindexing / Selection / Label manipulation
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. |
Missing data handling
Series.isna() |
Detect missing values. |
Series.notna() |
Detect existing (non-missing) values. |
Series.dropna([axis, inplace]) |
Return a new Series with missing values removed. |
Series.fillna([value, method, axis, …]) |
Fill NA/NaN values using the specified method |
Series.interpolate([method, axis, limit, …]) |
Interpolate values according to different methods. |
Reshaping, sorting
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. |
Combining / joining / merging
Series.append(to_append[, ignore_index, …]) |
Concatenate two or more Series. |
Series.replace([to_replace, value, inplace, …]) |
Replace values given in to_replace with value. |
Series.update(other) |
Modify Series in place using non-NA values from passed Series. |
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. |
Datetimelike Properties
Series.dt can be used to access the values of the series as datetimelike and return several properties. These can be accessed like Series.dt.<property>.
Datetime Properties
Datetime Methods
Series.dt.to_period(*args, **kwargs) |
Cast to PeriodIndex at a particular frequency. |
Series.dt.to_pydatetime() |
Return the data as an array of native Python datetime objects |
Series.dt.tz_localize(*args, **kwargs) |
Localize tz-naive DatetimeIndex to tz-aware DatetimeIndex. |
Series.dt.tz_convert(*args, **kwargs) |
Convert tz-aware DatetimeIndex from one time zone to another. |
Series.dt.normalize(*args, **kwargs) |
Convert times to midnight. |
Series.dt.strftime(*args, **kwargs) |
Convert to Index using specified date_format. |
Series.dt.round(*args, **kwargs) |
round the data to the specified freq. |
Series.dt.floor(*args, **kwargs) |
floor the data to the specified freq. |
Series.dt.ceil(*args, **kwargs) |
ceil the data to the specified freq. |
Series.dt.month_name(*args, **kwargs) |
Return the month names of the DateTimeIndex with specified locale. |
Series.dt.day_name(*args, **kwargs) |
Return the day names of the DateTimeIndex with specified locale. |
Timedelta Properties
Timedelta Methods
String handling
Series.str can be used to access the values of the series as strings and apply several methods to it. These can be accessed like Series.str.<function/property>.
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. |
Categorical
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.
Categorical data can be stored in a pandas.Categorical
Categorical(values[, categories, ordered, …]) |
Represents a categorical variable in classic R / S-plus fashion |
The alternative Categorical.from_codes() constructor can be used when you have the categories and integer codes already:
The dtype information is available on the Categorical
np.asarray(categorical) works by implementing the array interface. Be aware, that this converts the Categorical back to a NumPy array, so categories and order information is not preserved!
A Categorical can be stored in a Series or DataFrame. To create a Series of dtype category, use cat = s.astype(dtype) or Series(..., dtype=dtype) where dtype is either
If the Series is of dtype CategoricalDtype, Series.cat can be used to change the categorical data. This accessor is similar to the Series.dt or Series.str and has the following usable methods and properties:
Plotting
Series.plot is both a callable method and a namespace attribute for specific plotting methods of the form Series.plot.<kind>.
Series.plot([kind, ax, figsize, ….]) |
Series plotting accessor and method |
Series.hist([by, ax, grid, xlabelsize, …]) |
Draw histogram of the input series using matplotlib |
Serialization / IO / Conversion
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. |
Sparse
SparseSeries.to_coo([row_levels, …]) |
Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex. |
SparseSeries.from_coo(A[, dense_index]) |
Create a SparseSeries from a scipy.sparse.coo_matrix. |
DataFrame
Constructor
DataFrame([data, index, columns, dtype, copy]) |
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). |
Attributes and underlying data
Axes
Conversion
Indexing, iteration
DataFrame.head([n]) |
Return the first n rows. |
DataFrame.at |
Access a single value for a row/column label pair. |
DataFrame.iat |
Access a single value for a row/column pair by integer position. |
DataFrame.loc |
Access a group of rows and columns by label(s) or a boolean array. |
DataFrame.iloc |
Purely integer-location based indexing for selection by position. |
DataFrame.insert(loc, column, value[, …]) |
Insert column into DataFrame at specified location. |
DataFrame.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. |
For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.
Binary operator functions
DataFrame.add(other[, axis, level, fill_value]) |
Addition of dataframe and other, element-wise (binary operator add). |
DataFrame.sub(other[, axis, level, fill_value]) |
Subtraction of dataframe and other, element-wise (binary operator sub). |
DataFrame.mul(other[, axis, level, fill_value]) |
Multiplication of dataframe and other, element-wise (binary operator mul). |
DataFrame.div(other[, axis, level, fill_value]) |
Floating division of dataframe and other, element-wise (binary operator truediv). |
DataFrame.truediv(other[, axis, level, …]) |
Floating division of dataframe and other, element-wise (binary operator truediv). |
DataFrame.floordiv(other[, axis, level, …]) |
Integer division of dataframe and other, element-wise (binary operator floordiv). |
DataFrame.mod(other[, axis, level, fill_value]) |
Modulo of dataframe and other, element-wise (binary operator mod). |
DataFrame.pow(other[, axis, level, fill_value]) |
Exponential power of dataframe and other, element-wise (binary operator pow). |
DataFrame.dot(other) |
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. |
Function application, GroupBy & Window
DataFrame.apply(func[, axis, broadcast, …]) |
Apply a function along an axis of the DataFrame. |
DataFrame.applymap(func) |
Apply a function to a Dataframe elementwise. |
DataFrame.pipe(func, *args, **kwargs) |
Apply func(self, *args, **kwargs) |
DataFrame.agg(func[, axis]) |
Aggregate using one or more operations over the specified axis. |
DataFrame.aggregate(func[, axis]) |
Aggregate using one or more operations over the specified axis. |
DataFrame.transform(func, *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 |
Computations / Descriptive Stats
DataFrame.abs() |
Return a Series/DataFrame with absolute numeric value of each element. |
DataFrame.all([axis, bool_only, skipna, level]) |
Return whether all elements are True, potentially over an axis. |
DataFrame.any([axis, bool_only, skipna, level]) |
Return whether any element is True 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. |
Reindexing / Selection / Label manipulation
DataFrame.add_prefix(prefix) |
Prefix labels with string prefix. |
DataFrame.add_suffix(suffix) |
Suffix labels with string suffix. |
DataFrame.align(other[, join, axis, level, …]) |
Align two objects on their axes with the specified join method for each axis Index |
DataFrame.at_time(time[, asof]) |
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. |
Missing data handling
DataFrame.dropna([axis, how, thresh, …]) |
Remove missing values. |
DataFrame.fillna([value, method, axis, …]) |
Fill NA/NaN values using the specified method |
DataFrame.replace([to_replace, value, …]) |
Replace values given in to_replace with value. |
DataFrame.interpolate([method, axis, limit, …]) |
Interpolate values according to different methods. |
Reshaping, sorting, transposing
DataFrame.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. |
Combining / joining / merging
DataFrame.append(other[, ignore_index, …]) |
Append rows of other to the end of this frame, returning a new object. |
DataFrame.assign(**kwargs) |
Assign new columns to a DataFrame, returning a new object (a copy) with the new columns added to the original ones. |
DataFrame.join(other[, on, how, lsuffix, …]) |
Join columns with other DataFrame either on index or on a key column. |
DataFrame.merge(right[, how, on, left_on, …]) |
Merge DataFrame objects by performing a database-style join operation by columns or indexes. |
DataFrame.update(other[, join, overwrite, …]) |
Modify in place using non-NA values from another DataFrame. |
Time series-related
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. |
Plotting
DataFrame.plot is both a callable method and a namespace attribute for specific plotting methods of the form DataFrame.plot.<kind>.
DataFrame.plot([x, y, kind, ax, ….]) |
DataFrame plotting accessor and method |
DataFrame.plot.area([x, y]) |
Area plot |
DataFrame.plot.bar([x, y]) |
Vertical bar plot. |
DataFrame.plot.barh([x, y]) |
Make a horizontal bar plot. |
DataFrame.plot.box([by]) |
Make a box plot of the DataFrame columns. |
DataFrame.plot.density([bw_method, ind]) |
Generate Kernel Density Estimate plot using Gaussian kernels. |
DataFrame.plot.hexbin(x, y[, C, …]) |
Generate a hexagonal binning plot. |
DataFrame.plot.hist([by, bins]) |
Draw one histogram of the DataFrame’s columns. |
DataFrame.plot.kde([bw_method, ind]) |
Generate Kernel Density Estimate plot using Gaussian kernels. |
DataFrame.plot.line([x, y]) |
Plot DataFrame columns as lines. |
DataFrame.plot.pie([y]) |
Generate a pie plot. |
DataFrame.plot.scatter(x, y[, s, c]) |
Create a scatter plot with varying marker point size and color. |
Serialization / IO / Conversion
DataFrame.from_csv(path[, header, sep, …]) |
(DEPRECATED) Read CSV file. |
DataFrame.from_dict(data[, orient, dtype, …]) |
Construct DataFrame from dict of array-like or dicts. |
DataFrame.from_items(items[, columns, orient]) |
(DEPRECATED) Construct a dataframe from a list of tuples |
DataFrame.from_records(data[, index, …]) |
Convert structured or record ndarray to DataFrame |
DataFrame.info([verbose, buf, max_cols, …]) |
Print a concise summary of a DataFrame. |
DataFrame.to_parquet(fname[, engine, …]) |
Write a DataFrame to the binary parquet format. |
DataFrame.to_pickle(path[, compression, …]) |
Pickle (serialize) object to file. |
DataFrame.to_csv([path_or_buf, sep, na_rep, …]) |
Write 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. |
Sparse
Panel
Constructor
Panel([data, items, major_axis, minor_axis, …]) |
(DEPRECATED) Represents wide format panel data, stored as 3-dimensional array |
Attributes and underlying data
Axes
- items: axis 0; each item corresponds to a DataFrame contained inside
- major_axis: axis 1; the index (rows) of each of the DataFrames
- minor_axis: axis 2; the columns of each of the DataFrames
Panel.values |
Return a Numpy representation of the DataFrame. |
Panel.axes |
Return index label(s) of the internal NDFrame |
Panel.ndim |
Return an int representing the number of axes / array dimensions. |
Panel.size |
Return an int representing the number of elements in this object. |
Panel.shape |
Return a tuple of axis dimensions |
Panel.dtypes |
Return the dtypes in the DataFrame. |
Panel.ftypes |
Return the ftypes (indication of sparse/dense and dtype) in DataFrame. |
Panel.get_dtype_counts() |
Return counts of unique dtypes in this object. |
Panel.get_ftype_counts() |
(DEPRECATED) Return counts of unique ftypes in this object. |
Conversion
Panel.astype(dtype[, copy, errors]) |
Cast a pandas object to a specified dtype dtype. |
Panel.copy([deep]) |
Make a copy of this object’s indices and data. |
Panel.isna() |
Detect missing values. |
Panel.notna() |
Detect existing (non-missing) values. |
Getting and setting
Panel.get_value(*args, **kwargs) |
(DEPRECATED) Quickly retrieve single value at (item, major, minor) location |
Panel.set_value(*args, **kwargs) |
(DEPRECATED) Quickly set single value at (item, major, minor) location |
Indexing, iteration, slicing
Panel.at |
Access a single value for a row/column label pair. |
Panel.iat |
Access a single value for a row/column pair by integer position. |
Panel.loc |
Access a group of rows and columns by label(s) or a boolean array. |
Panel.iloc |
Purely integer-location based indexing for selection by position. |
Panel.__iter__() |
Iterate over infor axis |
Panel.iteritems() |
Iterate over (label, values) on info axis |
Panel.pop(item) |
Return item and drop from frame. |
Panel.xs(key[, axis]) |
Return slice of panel along selected axis |
Panel.major_xs(key) |
Return slice of panel along major axis |
Panel.minor_xs(key) |
Return slice of panel along minor axis |
For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.
Binary operator functions
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 |
Function application, GroupBy
Panel.apply(func[, axis]) |
Applies function along axis (or axes) of the Panel |
Panel.groupby(function[, axis]) |
Group data on given axis, returning GroupBy object |
Computations / Descriptive Stats
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. |
Reindexing / Selection / Label manipulation
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. |
Missing data handling
Panel.dropna([axis, how, inplace]) |
Drop 2D from panel, holding passed axis constant |
Reshaping, sorting, transposing
Panel.sort_index([axis, level, ascending, …]) |
Sort object by labels (along an axis) |
Panel.swaplevel([i, j, axis]) |
Swap levels i and j in a MultiIndex on a particular axis |
Panel.transpose(*args, **kwargs) |
Permute the dimensions of the Panel |
Panel.swapaxes(axis1, axis2[, copy]) |
Interchange axes and swap values axes appropriately |
Panel.conform(frame[, axis]) |
Conform input DataFrame to align with chosen axis pair. |
Combining / joining / merging
Panel.join(other[, how, lsuffix, rsuffix]) |
Join items with other Panel either on major and minor axes column |
Panel.update(other[, join, overwrite, …]) |
Modify Panel in place using non-NA values from passed Panel, or object coercible to Panel. |
Time series-related
Panel.asfreq(freq[, method, how, normalize, …]) |
Convert TimeSeries to specified frequency. |
Panel.shift([periods, freq, axis]) |
Shift index by desired number of periods with an optional time freq. |
Panel.resample(rule[, how, axis, …]) |
Convenience method for frequency conversion and resampling of time series. |
Panel.tz_convert(tz[, axis, level, copy]) |
Convert tz-aware axis to target time zone. |
Panel.tz_localize(tz[, axis, level, copy, …]) |
Localize tz-naive TimeSeries to target time zone. |
Serialization / IO / Conversion
Panel.from_dict(data[, intersect, orient, dtype]) |
Construct Panel from dict of DataFrame objects |
Panel.to_pickle(path[, compression, protocol]) |
Pickle (serialize) object to file. |
Panel.to_excel(path[, na_rep, engine]) |
Write each DataFrame in Panel to a separate excel sheet |
Panel.to_hdf(path_or_buf, key, **kwargs) |
Write the contained data to an HDF5 file using HDFStore. |
Panel.to_sparse(*args, **kwargs) |
NOT IMPLEMENTED: do not call this method, as sparsifying is not supported for Panel objects and will raise an error. |
Panel.to_frame([filter_observations]) |
Transform wide format into long (stacked) format as DataFrame whose columns are the Panel’s items and whose index is a MultiIndex formed of the Panel’s major and minor axes. |
Panel.to_clipboard([excel, sep]) |
Copy object to the system clipboard. |
Index
Many of these methods or variants thereof are available on the objects that contain an index (Series/DataFrame) and those should most likely be used before calling these methods directly.
Index |
Immutable ndarray implementing an ordered, sliceable set. |
Attributes
Modifying and Computations
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. |
Missing Values
Conversion
Index.astype(dtype[, copy]) |
Create an Index with values cast to dtypes. |
Index.item() |
return the first element of the underlying data as a python scalar |
Index.map(mapper[, na_action]) |
Map values using input correspondence (a dict, Series, or function). |
Index.ravel([order]) |
return an ndarray of the flattened values of the underlying data |
Index.tolist() |
Return a list of the values. |
Index.to_native_types([slicer]) |
Format specified values of self and return them. |
Index.to_series([index, name]) |
Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index |
Index.to_frame([index]) |
Create a DataFrame with a column containing the Index. |
Index.view([cls]) |
|
Sorting
Index.argsort(*args, **kwargs) |
Return the integer indicies that would sort the index. |
Index.searchsorted(value[, side, sorter]) |
Find indices where elements should be inserted to maintain order. |
Index.sort_values([return_indexer, ascending]) |
Return a sorted copy of the index. |
Time-specific operations
Index.shift([periods, freq]) |
Shift index by desired number of time frequency increments. |
Combining / joining / set operations
Index.append(other) |
Append a collection of Index options together |
Index.join(other[, how, level, …]) |
this is an internal non-public method |
Index.intersection(other) |
Form the intersection of two Index objects. |
Index.union(other) |
Form the union of two Index objects and sorts if possible. |
Index.difference(other) |
Return a new Index with elements from the index that are not in other. |
Index.symmetric_difference(other[, result_name]) |
Compute the symmetric difference of two Index objects. |
Selecting
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. |
Numeric Index
RangeIndex |
Immutable Index implementing a monotonic integer range. |
Int64Index |
Immutable ndarray implementing an ordered, sliceable set. |
UInt64Index |
Immutable ndarray implementing an ordered, sliceable set. |
Float64Index |
Immutable ndarray implementing an ordered, sliceable set. |
CategoricalIndex
Categorical Components
IntervalIndex
IntervalIndex |
Immutable Index implementing an ordered, sliceable set. |
IntervalIndex Components
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. |
MultiIndex
MultiIndex |
A multi-level, or hierarchical, index object for pandas objects |
IndexSlice |
Create an object to more easily perform multi-index slicing |
MultiIndex Constructors
MultiIndex Attributes
MultiIndex Components
MultiIndex Selecting
MultiIndex.get_loc(key[, method]) |
Get location for a label or a tuple of labels as an integer, slice or boolean mask. |
MultiIndex.get_indexer(target[, method, …]) |
Compute indexer and mask for new index given the current index. |
MultiIndex.get_level_values(level) |
Return vector of label values for requested level, equal to the length of the index. |
DatetimeIndex
DatetimeIndex |
Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information. |
Time/Date Components
Selecting
Time-specific operations
Conversion
TimedeltaIndex
TimedeltaIndex |
Immutable ndarray of timedelta64 data, represented internally as int64, and which can be boxed to timedelta objects |
Components
Conversion
PeriodIndex
PeriodIndex |
Immutable ndarray holding ordinal values indicating regular periods in time such as particular years, quarters, months, etc. |
Attributes
Methods
Scalars
Period
Period |
Represents a period of time |
Attributes
Methods
Period.asfreq |
Convert Period to desired frequency, either at the start or end of the interval |
Period.now |
|
Period.strftime |
Returns the string representation of the Period, depending on the selected fmt. |
Period.to_timestamp |
Return the Timestamp representation of the Period at the target frequency at the specified end (how) of the Period |
Timestamp
Timestamp |
Pandas replacement for datetime.datetime |
Properties
Methods
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. |
Interval
Interval |
Immutable object implementing an Interval, a bounded slice-like interval. |
Properties
Timedelta
Timedelta |
Represents a duration, the difference between two dates or times. |
Properties
Methods
Frequencies
to_offset(freq) |
Return DateOffset object from string or tuple representation or datetime.timedelta object |
Window
Rolling objects are returned by .rolling calls: pandas.DataFrame.rolling(), pandas.Series.rolling(), etc. Expanding objects are returned by .expanding calls: pandas.DataFrame.expanding(), pandas.Series.expanding(), etc. EWM objects are returned by .ewm calls: pandas.DataFrame.ewm(), pandas.Series.ewm(), etc.
Standard moving window functions
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. |
Standard expanding window functions
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. |
Exponentially-weighted moving window functions
EWM.mean(*args, **kwargs) |
exponential weighted moving average |
EWM.std([bias]) |
exponential weighted moving stddev |
EWM.var([bias]) |
exponential weighted moving variance |
EWM.corr([other, pairwise]) |
exponential weighted sample correlation |
EWM.cov([other, pairwise, bias]) |
exponential weighted sample covariance |
GroupBy
GroupBy objects are returned by groupby calls: pandas.DataFrame.groupby(), pandas.Series.groupby(), etc.
Indexing, iteration
Grouper([key, level, freq, axis, sort]) |
A Grouper allows the user to specify a groupby instruction for a target object |
Function application
GroupBy.apply(func, *args, **kwargs) |
Apply function func group-wise and combine the results together. |
GroupBy.aggregate(func, *args, **kwargs) |
|
GroupBy.transform(func, *args, **kwargs) |
|
GroupBy.pipe(func, *args, **kwargs) |
Apply a function func with arguments to this GroupBy object and return the function’s result. |
Computations / Descriptive Stats
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 |
The following methods are available in both SeriesGroupBy and DataFrameGroupBy objects, but may differ slightly, usually in that the DataFrameGroupBy version usually permits the specification of an axis argument, and often an argument indicating whether to restrict application to columns of a specific data type.
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. |
The following methods are available only for SeriesGroupBy objects.
The following methods are available only for DataFrameGroupBy objects.
Resampling
Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample().
Indexing, iteration
Function application
Resampler.apply(arg, *args, **kwargs) |
Aggregate using one or more operations over the specified axis. |
Resampler.aggregate(arg, *args, **kwargs) |
Aggregate using one or more operations over the specified axis. |
Resampler.transform(arg, *args, **kwargs) |
Call function producing a like-indexed Series on each group and return a Series with the transformed values |
Resampler.pipe(func, *args, **kwargs) |
Apply a function func with arguments to this Resampler object and return the function’s result. |
Upsampling
Computations / Descriptive Stats
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 |
Style
Styler objects are returned by pandas.DataFrame.style.
Styler Constructor
Styler(data[, precision, table_styles, …]) |
Helps style a DataFrame or Series according to the data with HTML and CSS. |
Styler.from_custom_template(searchpath, name) |
Factory function for creating a subclass of Styler with a custom template and Jinja environment. |
Styler Attributes
Style Application
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. |
Builtin Styles
Styler.highlight_max([subset, color, axis]) |
Highlight the maximum by shading the background |
Styler.highlight_min([subset, color, axis]) |
Highlight the minimum by shading the background |
Styler.highlight_null([null_color]) |
Shade the background null_color for missing values. |
Styler.background_gradient([cmap, low, …]) |
Color the background in a gradient according to the data in each column (optionally row). |
Styler.bar([subset, axis, color, width, align]) |
Color the background color proptional to the values in each column. |
Style Export and Import
Styler.render(**kwargs) |
Render the built up styles to HTML |
Styler.export() |
Export the styles to applied to the current Styler. |
Styler.use(styles) |
Set the styles on the current Styler, possibly using styles from Styler.export. |
Styler.to_excel(excel_writer[, sheet_name, …]) |
Write Styler to an excel sheet |
Plotting
The following functions are contained in the pandas.plotting module.
andrews_curves(frame, class_column[, ax, …]) |
Generates a matplotlib plot of Andrews curves, for visualising clusters of multivariate data. |
bootstrap_plot(series[, fig, size, samples]) |
Bootstrap plot on mean, median and mid-range statistics. |
deregister_matplotlib_converters() |
Remove pandas’ formatters and converters |
lag_plot(series[, lag, ax]) |
Lag plot for time series. |
parallel_coordinates(frame, class_column[, …]) |
Parallel coordinates plotting. |
radviz(frame, class_column[, ax, color, …]) |
Plot a multidimensional dataset in 2D. |
register_matplotlib_converters([explicit]) |
Register Pandas Formatters and Converters with matplotlib |
scatter_matrix(frame[, alpha, figsize, ax, …]) |
Draw a matrix of scatter plots. |
General utility functions
Working with options
describe_option(pat[, _print_desc]) |
Prints the description for one or more registered options. |
reset_option(pat) |
Reset one or more options to their default value. |
get_option(pat) |
Retrieves the value of the specified option. |
set_option(pat, value) |
Sets the value of the specified option. |
option_context(*args) |
Context manager to temporarily set options in the with statement context. |
Testing functions
Exceptions and warnings
Dtype introspection
Iterable introspection
Scalar introspection
Extensions
These are primarily intended for library authors looking to extend pandas objects.