This page gives an overview of all public pandas objects, functions and methods. In general, all classes and functions exposed in the top-level pandas.* namespace are regarded as public.
Further some of the subpackages are public, including pandas.errors, pandas.plotting, and pandas.testing. Certain functions in the the pandas.io and pandas.tseries submodules are public as well (those mentioned in the documentation). Further, the 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 considered to be PRIVATE. Stability of functionality in those modules in not guaranteed.
read_pickle(path[, compression]) Load pickled pandas object (or any other pickled object) from the specified 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 Clipboard¶ read_clipboard([sep]) Read text from clipboard and pass to read_table. Excel¶ read_excel(io[, sheetname, header, ...]) Read an Excel table into a pandas DataFrame ExcelFile.parse([sheetname, header, ...]) 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 Feather¶ read_feather(path) Load a feather-format object from the file path 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 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, ...]) Return indices of half-open bins to which each value of x belongs. 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 input values 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¶ isnull(obj) Detect missing values (NaN in numeric arrays, None/NaN in object arrays) notnull(obj) Replacement for numpy.isfinite / -numpy.isnan which is suitable for use on object arrays. 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 datetime index, with day (calendar) as the default bdate_range([start, end, periods, freq, tz, ...]) Return a fixed frequency datetime index, with business day as the default period_range([start, end, periods, freq, name]) Return a fixed frequency datetime index, with day (calendar) as the default timedelta_range([start, end, periods, freq, ...]) Return a fixed frequency timedelta index, with day as the default infer_freq(index[, warn]) Infer the most likely frequency given the input index. Top-level evaluation¶ eval(expr[, parser, engine, truediv, ...]) Evaluate a Python expression as a string using various backends. Series¶ Constructor¶ Series([data, index, dtype, name, copy, ...]) One-dimensional ndarray with axis labels (including time series). Attributes¶
Series.values Return Series as ndarray or ndarray-like Series.dtype return the dtype object of the underlying data Series.ftype return if the data is sparse|dense Series.shape return a tuple of the shape of the underlying data Series.nbytes return the number of bytes in the underlying data Series.ndim return the number of dimensions of the underlying data, Series.size return the number of elements in the underlying data Series.strides return the strides of the underlying data Series.itemsize return the size of the dtype of the item of the underlying data Series.base return the base object if the memory of the underlying data is Series.T return the transpose, which is by definition self Series.memory_usage([index, deep]) Memory usage of the Series Conversion¶ Series.astype(dtype[, copy, errors]) Cast object to input numpy.dtype Series.copy([deep]) Make a copy of this objects data. Series.isnull() Return a boolean same-sized object indicating if the values are null. Series.notnull() Return a boolean same-sized object indicating if the values are not null. Indexing, iteration¶ Series.get(key[, default]) Get item from object for given key (DataFrame column, Panel slice, etc.). Series.at Fast label-based scalar accessor Series.iat Fast integer location scalar accessor. Series.loc Purely label-location based indexer for selection by label. Series.iloc Purely integer-location based indexing for selection by position. Series.__iter__() provide iteration over the values of the Series Series.iteritems() Lazily iterate over (index, value) tuples
For more information on .at, .iat, .loc, and .iloc, see the indexing documentation.
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 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). Function application, GroupBy & Window¶ Series.apply(func[, convert_dtype, args]) Invoke function on values of Series. Series.aggregate(func[, axis]) Aggregate using callable, string, dict, or list of string/callables Series.transform(func, *args, **kwargs) Call function producing a like-indexed NDFrame Series.map(arg[, na_action]) Map values of Series using input correspondence (which can be 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, freq, ...]) Provides rolling window calculcations. Series.expanding([min_periods, freq, ...]) Provides expanding transformations. Series.ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions Computations / Descriptive Stats¶ Series.abs() Return an object with absolute value taken–only applicable to objects that are all numeric. Series.all([axis, bool_only, skipna, level]) Return whether all elements are True over requested 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]) Trim values at input threshold(s). Series.clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated. Series.clip_upper(threshold[, axis]) 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 max over requested axis. Series.cummin([axis, skipna]) Return cumulative minimum over requested axis. Series.cumprod([axis, skipna]) Return cumulative product over requested axis. Series.cumsum([axis, skipna]) Return cumulative sum over requested 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]) 1st discrete difference of object 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, ...]) Percent change over given number of periods. Series.prod([axis, skipna, level, numeric_only]) 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 Series.std([axis, skipna, level, ddof, ...]) Return sample standard deviation over requested axis. Series.sum([axis, skipna, level, numeric_only]) Return the sum of the values for the requested axis Series.var([axis, skipna, level, ddof, ...]) Return unbiased variance over requested axis. Series.unique() Return unique values in the 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 Series.is_monotonic_increasing Return boolean if values in the object are Series.is_monotonic_decreasing Return boolean if values in the object are Series.value_counts([normalize, sort, ...]) Returns object containing counts of unique values. Reindexing / Selection / Label manipulation¶ Series.align(other[, join, axis, level, ...]) Align two object on their axes with the Series.drop(labels[, axis, level, inplace, ...]) Return new object with labels in requested axis removed. Series.drop_duplicates([keep, inplace]) Return Series with duplicate values removed Series.duplicated([keep]) Return boolean Series denoting duplicate 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]) Returns first n rows Series.idxmax([axis, skipna]) Index of first occurrence of maximum of values. Series.idxmin([axis, skipna]) Index of first occurrence of minimum of values. Series.isin(values) Return a boolean Series showing whether each element in the Series is exactly contained in the passed sequence of values. 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 axes input function or functions. Series.rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions. Series.reset_index([level, drop, name, inplace]) Analogous to the pandas.DataFrame.reset_index() function, see docstring there. Series.sample([n, frac, replace, weights, ...]) Returns a random sample of items from an axis of object. Series.select(crit[, axis]) Return data corresponding to axis labels matching criteria Series.take(indices[, axis, convert, is_copy]) return Series corresponding to requested indices Series.tail([n]) Returns last n rows Series.truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular 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. Missing data handling¶ Series.dropna([axis, inplace]) Return Series without null values 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.reorder_levels(order) Rearrange index levels using input order. Series.sort_values([axis, ascending, ...]) Sort by the values along either axis Series.sort_index([axis, level, ascending, ...]) Sort object by labels (along an axis) 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. 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. 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
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]) 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, ...]) Return boolean Series/array whether given pattern/regex is contained in each string in the Series/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]) Return boolean Series indicating whether each string in the Series/Index ends with passed 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 lists, tuples, or strings in each element in the Series/Index. 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, flags]) 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 slice of each string in the Series/Index with another string. Series.str.split([pat, n, expand]) Split each string (a la re.split) in the Series/Index by given pattern, propagating NA values. 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]) Return boolean Series/array indicating whether each string in the Series/Index starts with passed 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¶
If the Series is of dtype category, Series.cat can be used to change the the categorical data. This accessor is similar to the Series.dt or Series.str and has the following usable methods and properties:
To create a Series of dtype category, use cat = s.astype("category").
The following two Categorical constructors are considered API but should only be used when adding ordering information or special categories is need at creation time of the categorical data:
Categorical(values[, categories, ordered, ...]) Represents a categorical variable in classic R / S-plus fashion
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!
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.from_csv(path[, sep, parse_dates, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). Series.to_pickle(path[, compression]) Pickle (serialize) object to input file path. Series.to_csv([path, index, sep, na_rep, ...]) Write Series to a comma-separated values (csv) file Series.to_dict() Convert Series to {label -> value} dict 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[, flavor, 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]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example. 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
Indexing, iteration¶
- index: row labels
- columns: column labels
DataFrame.head([n]) Returns first n rows DataFrame.at Fast label-based scalar accessor DataFrame.iat Fast integer location scalar accessor. DataFrame.loc Purely label-location based indexer for selection by label. DataFrame.iloc Purely integer-location based indexing for selection by position. DataFrame.insert(loc, column, value[, ...]) Insert column into DataFrame at specified location. DataFrame.__iter__() Iterate over infor axis DataFrame.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]) Returns last n rows DataFrame.xs(key[, axis, level, drop_level]) Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. 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.
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.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 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, ...]) Applies function along input axis of DataFrame. DataFrame.applymap(func) Apply a function to a DataFrame that is intended to operate elementwise, i.e. DataFrame.aggregate(func[, axis]) Aggregate using callable, string, dict, or list of string/callables DataFrame.transform(func, *args, **kwargs) Call function producing a like-indexed NDFrame 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 calculcations. DataFrame.expanding([min_periods, freq, ...]) Provides expanding transformations. DataFrame.ewm([com, span, halflife, alpha, ...]) Provides exponential weighted functions Computations / Descriptive Stats¶ DataFrame.abs() Return an object with absolute value taken–only applicable to objects that are all numeric. DataFrame.all([axis, bool_only, skipna, level]) Return whether all elements are True over requested axis DataFrame.any([axis, bool_only, skipna, level]) Return whether any element is True over requested axis DataFrame.clip([lower, upper, axis]) Trim values at input threshold(s). DataFrame.clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated. DataFrame.clip_upper(threshold[, axis]) Return copy of input with values above given value(s) truncated. 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]) Return Series with number of non-NA/null observations over requested axis. DataFrame.cov([min_periods]) Compute pairwise covariance of columns, excluding NA/null values DataFrame.cummax([axis, skipna]) Return cumulative max over requested axis. DataFrame.cummin([axis, skipna]) Return cumulative minimum over requested axis. DataFrame.cumprod([axis, skipna]) Return cumulative product over requested axis. DataFrame.cumsum([axis, skipna]) Return cumulative sum over requested 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]) 1st discrete difference of object DataFrame.eval(expr[, inplace]) Evaluate an expression in the context of the calling DataFrame instance. DataFrame.kurt([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, ...]) Percent change over given number of periods. DataFrame.prod([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 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. Reindexing / Selection / Label manipulation¶ DataFrame.add_prefix(prefix) Concatenate prefix string with panel items names. DataFrame.add_suffix(suffix) Concatenate suffix string with panel items names. DataFrame.align(other[, join, axis, level, ...]) Align two object on their axes with the DataFrame.drop(labels[, axis, level, ...]) Return new object with labels in requested axis removed. DataFrame.drop_duplicates([subset, keep, ...]) Return DataFrame with duplicate rows removed, optionally only DataFrame.duplicated([subset, keep]) Return boolean Series denoting duplicate rows, optionally only 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]) Returns 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([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([index, columns]) Alter axes input function or functions. DataFrame.rename_axis(mapper[, axis, copy, ...]) Alter index and / or columns using input function or functions. 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, ...]) Returns a random sample of items from an axis of object. DataFrame.select(crit[, axis]) Return data corresponding to axis labels matching criteria DataFrame.set_index(keys[, drop, append, ...]) Set the DataFrame index (row labels) using one or more existing columns. DataFrame.tail([n]) Returns last n rows DataFrame.take(indices[, axis, convert, is_copy]) Analogous to ndarray.take DataFrame.truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular index value. Missing data handling¶ DataFrame.dropna([axis, how, thresh, ...]) Return object with labels on given axis omitted where alternately any 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’. Reshaping, sorting, transposing¶ DataFrame.pivot([index, columns, values]) Reshape data (produce a “pivot” table) based on column values. 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]) Get the rows of a DataFrame sorted by the n largest values of columns. 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]) Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels. 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.melt([id_vars, value_vars, ...]) “Unpivots” a DataFrame from wide format to long format, optionally DataFrame.T Transpose index and columns DataFrame.to_panel() Transform long (stacked) format (DataFrame) into wide (3D, Panel) format. DataFrame.to_xarray() Return an xarray object from the pandas object. 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 all the original columns in addition to the new 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 DataFrame in place using non-NA values from passed 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 DataFrame.shift([periods, freq, axis]) Shift index by desired number of periods with an optional time freq DataFrame.first_valid_index() Return label for first non-NA/null value DataFrame.last_valid_index() Return label 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 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]) Horizontal bar plot DataFrame.plot.box([by]) Boxplot DataFrame.plot.density(**kwds) Kernel Density Estimate plot DataFrame.plot.hexbin(x, y[, C, ...]) Hexbin plot DataFrame.plot.hist([by, bins]) Histogram DataFrame.plot.kde(**kwds) Kernel Density Estimate plot DataFrame.plot.line([x, y]) Line plot DataFrame.plot.pie([y]) Pie chart DataFrame.plot.scatter(x, y[, s, c]) Scatter plot DataFrame.boxplot([column, by, ax, ...]) Make a box plot from DataFrame column optionally grouped by some columns or DataFrame.hist(data[, column, by, grid, ...]) Draw histogram of the DataFrame’s series using matplotlib / pylab. Serialization / IO / Conversion¶ DataFrame.from_csv(path[, header, sep, ...]) Read CSV file (DISCOURAGED, please use pandas.read_csv() instead). DataFrame.from_dict(data[, orient, dtype]) Construct DataFrame from dict of array-like or dicts DataFrame.from_items(items[, columns, orient]) Convert (key, value) pairs to DataFrame. DataFrame.from_records(data[, index, ...]) Convert structured or record ndarray to DataFrame DataFrame.info([verbose, buf, max_cols, ...]) Concise summary of a DataFrame. DataFrame.to_pickle(path[, compression]) Pickle (serialize) object to input file path. 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[, flavor, ...]) Write records stored in a DataFrame to a SQL database. DataFrame.to_dict([orient]) Convert DataFrame to 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, ...]) A class for writing Stata binary dta files from array-like objects 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 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]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example. Panel¶ Constructor¶ Panel([data, items, major_axis, minor_axis, ...]) Represents wide format panel data, stored as 3-dimensional array Attributes and underlying data¶
Axes
Conversion¶
- 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.astype(dtype[, copy, errors]) Cast object to input numpy.dtype Panel.copy([deep]) Make a copy of this objects data. Panel.isnull() Return a boolean same-sized object indicating if the values are null. Panel.notnull() Return a boolean same-sized object indicating if the values are not null. Getting and setting¶ Panel.get_value(*args, **kwargs) Quickly retrieve single value at (item, major, minor) location Panel.set_value(*args, **kwargs) Quickly set single value at (item, major, minor) location Indexing, iteration, slicing¶ Panel.at Fast label-based scalar accessor Panel.iat Fast integer location scalar accessor. Panel.loc Purely label-location based indexer for selection by label. 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.
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 an object with absolute value taken–only applicable to objects that are all numeric. Panel.clip([lower, upper, axis]) Trim values at input threshold(s). Panel.clip_lower(threshold[, axis]) Return copy of the input with values below given value(s) truncated. Panel.clip_upper(threshold[, axis]) 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 max over requested axis. Panel.cummin([axis, skipna]) Return cumulative minimum over requested axis. Panel.cumprod([axis, skipna]) Return cumulative product over requested axis. Panel.cumsum([axis, skipna]) Return cumulative sum over requested 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, ...]) Percent change over given number of periods. Panel.prod([axis, skipna, level, numeric_only]) 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 Panel.sum([axis, skipna, level, numeric_only]) 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) Concatenate prefix string with panel items names. Panel.add_suffix(suffix) Concatenate suffix string with panel items names. Panel.drop(labels[, axis, level, inplace, ...]) Return new object with labels in requested axis removed. 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([items, major_axis, minor_axis]) 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, ...]) Returns a random sample of items from an axis of object. Panel.select(crit[, axis]) Return data corresponding to axis labels matching criteria Panel.take(indices[, axis, convert, is_copy]) Analogous to ndarray.take Panel.truncate([before, after, axis, copy]) Truncates a sorted NDFrame before and/or after some particular index value. Missing data handling¶ Panel.dropna([axis, how, inplace]) Drop 2D from panel, holding passed axis constant Panel.fillna([value, method, axis, inplace, ...]) Fill NA/NaN values using the specified method 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]) Pickle (serialize) object to input file path. 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_xarray() Return an xarray object from the pandas object. Panel.to_clipboard([excel, sep]) Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example. 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. 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]) Return boolean np.ndarray denoting duplicate 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 Index.insert(loc, item) Make new Index inserting new item at location. Index.min() The minimum value of the object Index.max() The maximum value of the object Index.reindex(target[, method, level, ...]) Create index with target’s values (move/add/delete values as necessary) Index.repeat(repeats, *args, **kwargs) Repeat elements of an Index. Index.where(cond[, other])
New in version 0.19.0.
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() Return unique values in the object. Index.nunique([dropna]) Return number of unique elements in the object. Index.value_counts([normalize, sort, ...]) Returns object containing counts of unique values. Sorting¶ Index.argsort(*args, **kwargs) Returns the indices that would sort the index and its underlying data. Index.sort_values([return_indexer, ascending]) Return sorted copy of Index Time-specific operations¶ Index.shift([periods, freq]) Shift Index containing datetime objects by input number of periods and 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.get_indexer(target[, method, limit, ...]) Compute indexer and mask for new index given the current index. 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 Index.get_loc(key[, method, tolerance]) Get integer location for requested label. Index.get_value(series, key) Fast lookup of value from 1-dimensional ndarray. Index.isin(values[, level]) Compute boolean array of whether each index value is found in the passed set of values. Index.slice_indexer([start, end, step, kind]) For an ordered Index, compute the slice indexer for input labels and Index.slice_locs([start, end, step, kind]) Compute slice locations for input labels. IntervalIndex¶ IntervalIndex Immutable Index implementing an ordered, sliceable set. IntervalIndex Components¶ MultiIndex¶ MultiIndex A multi-level, or hierarchical, index object for pandas objects IndexSlice Create an object to more easily perform multi-index slicing MultiIndex Components¶ MultiIndex.from_arrays(arrays[, sortorder, ...]) Convert arrays to MultiIndex MultiIndex.from_tuples(tuples[, sortorder, ...]) Convert list of tuples to MultiIndex MultiIndex.from_product(iterables[, ...]) Make a MultiIndex from the cartesian product of multiple iterables MultiIndex.set_levels(levels[, level, ...]) Set new levels on MultiIndex. MultiIndex.set_labels(labels[, level, ...]) Set new labels on MultiIndex. MultiIndex.to_hierarchical(n_repeat[, n_shuffle]) Return a MultiIndex reshaped to conform to the shapes given by n_repeat and n_shuffle. MultiIndex.to_frame([index]) Create a DataFrame with the columns the levels of the MultiIndex MultiIndex.is_lexsorted() Return True if the labels are lexicographically sorted MultiIndex.droplevel([level]) Return Index with requested level removed. MultiIndex.swaplevel([i, j]) Swap level i with level j. MultiIndex.reorder_levels(order) Rearrange levels using input order. MultiIndex.remove_unused_levels() create a new MultiIndex from the current that removing 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-specific operations¶ TimedeltaIndex¶ TimedeltaIndex Immutable ndarray of timedelta64 data, represented internally as int64, and 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.
Rolling.count() rolling count of number of non-NaN Rolling.sum(*args, **kwargs) rolling sum Rolling.mean(*args, **kwargs) rolling mean Rolling.median(**kwargs) rolling median Rolling.var([ddof]) rolling variance Rolling.std([ddof]) rolling standard deviation Rolling.min(*args, **kwargs) 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) Unbiased rolling kurtosis Rolling.apply(func[, args, kwargs]) rolling function apply Rolling.quantile(quantile, **kwargs) rolling quantile Window.mean(*args, **kwargs) window mean Window.sum(*args, **kwargs) window sum Standard expanding window functions¶ Expanding.count(**kwargs) expanding count of number of non-NaN Expanding.sum(*args, **kwargs) expanding sum Expanding.mean(*args, **kwargs) expanding mean Expanding.median(**kwargs) expanding median Expanding.var([ddof]) expanding variance Expanding.std([ddof]) expanding standard deviation Expanding.min(*args, **kwargs) 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) Unbiased expanding kurtosis Expanding.apply(func[, args, kwargs]) expanding function apply Expanding.quantile(quantile, **kwargs) 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.
Grouper([key, level, freq, axis, sort]) A Grouper allows the user to specify a groupby instruction for a target Computations / Descriptive Stats¶ 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.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 GroupBy.prod(**kwargs) Compute prod of group values 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 callable, string, dict, or list of string/callables DataFrameGroupBy.all Return whether all elements are True over requested axis DataFrameGroupBy.any Return whether any element is True over requested axis 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) Parameters: DataFrameGroupBy.diff 1st discrete difference of object DataFrameGroupBy.ffill([limit]) Forward fill the values DataFrameGroupBy.fillna Fill NA/NaN values using the specified method DataFrameGroupBy.hist Draw histogram of the DataFrame’s series using matplotlib / pylab. 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 Percent change over given number of periods. 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 Compute numerical data ranks (1 through n) along axis. DataFrameGroupBy.resample(rule, *args, **kwargs) Provide resampling when using a TimeGrouper DataFrameGroupBy.shift([periods, freq, axis]) Shift each group by periods observations DataFrameGroupBy.size() Compute group sizes DataFrameGroupBy.skew Return unbiased skew over requested axis DataFrameGroupBy.take Analogous to ndarray.take 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.
Resampler objects are returned by resample calls: pandas.DataFrame.resample(), pandas.Series.resample().
Resampler.apply(arg, *args, **kwargs) Aggregate using callable, string, dict, or list of string/callables Resampler.aggregate(arg, *args, **kwargs) Aggregate using callable, string, dict, or list of string/callables Resampler.transform(arg, *args, **kwargs) Call function producing a like-indexed Series on each group and return Computations / Descriptive Stats¶ Style¶
Styler objects are returned by pandas.DataFrame.style.
Styler(data[, precision, table_styles, ...]) Helps style a DataFrame or Series according to the data with HTML and CSS. 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.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_caption(caption) Se the caption on a Styler Styler.set_properties([subset]) Convience 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. 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. Exceptions and warnings¶
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