Enter search terms or a module, class or function name.
read_pickle(path) | Load pickled pandas object (or any other pickled object) from the specified |
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_clipboard(\*\*kwargs) | Read text from clipboard and pass to read_table. |
read_excel(io[, sheetname, header, ...]) | Read an Excel table into a pandas DataFrame |
ExcelFile.parse([sheetname, header, ...]) | Parse specified sheet(s) into a DataFrame |
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 |
read_hdf(path_or_buf[, key]) | 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 |
read_sas(filepath_or_buffer[, format, ...]) | Read SAS files stored as either XPORT or SAS7BDAT format files. |
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. |
read_gbq(query[, project_id, index_col, ...]) | Load data from Google BigQuery. |
to_gbq(dataframe, destination_table, project_id) | Write a DataFrame to a Google BigQuery table. |
read_stata(filepath_or_buffer[, ...]) | Read Stata file into DataFrame |
StataReader.data(\*\*kwargs) | DEPRECATED: Reads observations from Stata file, converting them into a dataframe |
StataReader.data_label() | Returns data label of Stata file |
StataReader.value_labels() | Returns a dict, associating each variable name a dict, associating |
StataReader.variable_labels() | Returns variable labels as a dict, associating each variable name |
StataWriter.write_file() |
melt(frame[, id_vars, value_vars, var_name, ...]) | “Unpivots” a DataFrame from wide format to long format, optionally leaving |
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 ofx 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 |
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. |
to_numeric(arg[, errors, downcast]) | Convert argument to a numeric type. |
to_datetime(\*args, \*\*kwargs) | Convert argument to datetime. |
to_timedelta(\*args, \*\*kwargs) | 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. |
Series([data, index, dtype, name, copy, ...]) | One-dimensional ndarray with axis labels (including time series). |
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 |
Series.astype(dtype[, copy, raise_on_error]) | 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. |
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.ix | A primarily label-location based indexer, with integer position fallback. |
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,.ix,.loc, and.iloc, see theindexing documentation.
Series.add(other[, level, fill_value, axis]) | Addition of series and other, element-wise (binary operatoradd). |
Series.sub(other[, level, fill_value, axis]) | Subtraction of series and other, element-wise (binary operatorsub). |
Series.mul(other[, level, fill_value, axis]) | Multiplication of series and other, element-wise (binary operatormul). |
Series.div(other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operatortruediv). |
Series.truediv(other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operatortruediv). |
Series.floordiv(other[, level, fill_value, axis]) | Integer division of series and other, element-wise (binary operatorfloordiv). |
Series.mod(other[, level, fill_value, axis]) | Modulo of series and other, element-wise (binary operatormod). |
Series.pow(other[, level, fill_value, axis]) | Exponential power of series and other, element-wise (binary operatorpow). |
Series.radd(other[, level, fill_value, axis]) | Addition of series and other, element-wise (binary operatorradd). |
Series.rsub(other[, level, fill_value, axis]) | Subtraction of series and other, element-wise (binary operatorrsub). |
Series.rmul(other[, level, fill_value, axis]) | Multiplication of series and other, element-wise (binary operatorrmul). |
Series.rdiv(other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operatorrtruediv). |
Series.rtruediv(other[, level, fill_value, axis]) | Floating division of series and other, element-wise (binary operatorrtruediv). |
Series.rfloordiv(other[, level, fill_value, ...]) | Integer division of series and other, element-wise (binary operatorrfloordiv). |
Series.rmod(other[, level, fill_value, axis]) | Modulo of series and other, element-wise (binary operatorrmod). |
Series.rpow(other[, level, fill_value, axis]) | Exponential power of series and other, element-wise (binary operatorrpow). |
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 operatorlt). |
Series.gt(other[, level, fill_value, axis]) | Greater than of series and other, element-wise (binary operatorgt). |
Series.le(other[, level, fill_value, axis]) | Less than or equal to of series and other, element-wise (binary operatorle). |
Series.ge(other[, level, fill_value, axis]) | Greater than or equal to of series and other, element-wise (binary operatorge). |
Series.ne(other[, level, fill_value, axis]) | Not equal to of series and other, element-wise (binary operatorne). |
Series.eq(other[, level, fill_value, axis]) | Equal to of series and other, element-wise (binary operatoreq). |
Series.apply(func[, convert_dtype, args]) | Invoke function on values of Series. |
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 |
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 withother 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]) | Generate various summary statistics, 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() | Returns the mode(s) of the dataset. |
Series.nlargest(\*args, \*\*kwargs) | Return the largestn elements. |
Series.nsmallest(\*args, \*\*kwargs) | Return the smallestn 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 np.ndarray of 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. |
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(\*args, \*\*kwargs) | Return Series with duplicate values removed |
Series.duplicated(\*args, \*\*kwargs) | 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 booleanSeries showing whether each element in theSeries is exactly contained in the passed sequence ofvalues. |
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 thepandas.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. |
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. |
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.sortlevel([level, ascending, ...]) | Sort Series with MultiIndex by chosen level. |
Series.swaplevel([i, j, copy]) | Swap levels i and j in a MultiIndex |
Series.unstack([level, fill_value]) | Unstack, a.k.a. |
Series.searchsorted(v[, side, sorter]) | Find indices where elements should be inserted to maintain order. |
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, normalize]) | Convert TimeSeries to specified frequency. |
Series.asof(where[, subset]) | The last row without any NaN is taken (or the last row without |
Series.shift([periods, freq, axis]) | Shift index by desired number of periods with an optional time freq |
Series.first_valid_index() | Return label for first non-NA/null value |
Series.last_valid_index() | Return label 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(\*args, \*\*kwargs) | Localize tz-naive TimeSeries to target time zone. |
Series.dt can be used to access the values of the series asdatetimelike and return several properties.These can be accessed likeSeries.dt.<property>.
Datetime Properties
Series.dt.date | Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information). |
Series.dt.time | Returns numpy array of datetime.time. |
Series.dt.year | The year of the datetime |
Series.dt.month | The month as January=1, December=12 |
Series.dt.day | The days of the datetime |
Series.dt.hour | The hours of the datetime |
Series.dt.minute | The minutes of the datetime |
Series.dt.second | The seconds of the datetime |
Series.dt.microsecond | The microseconds of the datetime |
Series.dt.nanosecond | The nanoseconds of the datetime |
Series.dt.week | The week ordinal of the year |
Series.dt.weekofyear | The week ordinal of the year |
Series.dt.dayofweek | The day of the week with Monday=0, Sunday=6 |
Series.dt.weekday | The day of the week with Monday=0, Sunday=6 |
Series.dt.weekday_name | The name of day in a week (ex: Friday) |
Series.dt.dayofyear | The ordinal day of the year |
Series.dt.quarter | The quarter of the date |
Series.dt.is_month_start | Logical indicating if first day of month (defined by frequency) |
Series.dt.is_month_end | Logical indicating if last day of month (defined by frequency) |
Series.dt.is_quarter_start | Logical indicating if first day of quarter (defined by frequency) |
Series.dt.is_quarter_end | Logical indicating if last day of quarter (defined by frequency) |
Series.dt.is_year_start | Logical indicating if first day of year (defined by frequency) |
Series.dt.is_year_end | Logical indicating if last day of year (defined by frequency) |
Series.dt.is_leap_year | Logical indicating if the date belongs to a leap year |
Series.dt.daysinmonth | The number of days in the month |
Series.dt.days_in_month | The number of days in the month |
Series.dt.tz | |
Series.dt.freq | get/set the frequncy of the Index |
Datetime Methods
Series.dt.to_period(\*args, \*\*kwargs) | Cast to PeriodIndex at a particular frequency |
Series.dt.to_pydatetime() | |
Series.dt.tz_localize(\*args, \*\*kwargs) | Localize tz-naive DatetimeIndex to given time zone (using |
Series.dt.tz_convert(\*args, \*\*kwargs) | Convert tz-aware DatetimeIndex from one time zone to another (using |
Series.dt.normalize(\*args, \*\*kwargs) | Return DatetimeIndex with times to midnight. |
Series.dt.strftime(\*args, \*\*kwargs) | Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library. |
Series.dt.round(\*args, \*\*kwargs) | round the index to the specified freq |
Series.dt.floor(\*args, \*\*kwargs) | floor the index to the specified freq |
Series.dt.ceil(\*args, \*\*kwargs) | ceil the index to the specified freq |
Timedelta Properties
Series.dt.days | Number of days for each element. |
Series.dt.seconds | Number of seconds (>= 0 and less than 1 day) for each element. |
Series.dt.microseconds | Number of microseconds (>= 0 and less than 1 second) for each element. |
Series.dt.nanoseconds | Number of nanoseconds (>= 0 and less than 1 microsecond) for each element. |
Series.dt.components | Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas. |
Timedelta Methods
Series.dt.to_pytimedelta() | |
Series.dt.total_seconds(\*args, \*\*kwargs) | Total duration of each element expressed in seconds. |
Series.str can be used to access the values of the series asstrings and apply several methods to it. These can be accessed likeSeries.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, ...]) | Deprecated: Find groups in each string in the Series/Index using passed 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 ofsep, 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 ofsep, 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. |
If the Series is of dtypecategory,Series.cat can be used to change the the categoricaldata. This accessor is similar to theSeries.dt orSeries.str and has thefollowing usable methods and properties:
Series.cat.categories | The categories of this categorical. |
Series.cat.ordered | Gets the ordered attribute |
Series.cat.codes |
Series.cat.rename_categories(\*args, \*\*kwargs) | Renames categories. |
Series.cat.reorder_categories(\*args, \*\*kwargs) | Reorders categories as specified in new_categories. |
Series.cat.add_categories(\*args, \*\*kwargs) | Add new categories. |
Series.cat.remove_categories(\*args, \*\*kwargs) | Removes the specified categories. |
Series.cat.remove_unused_categories(\*args, ...) | Removes categories which are not used. |
Series.cat.set_categories(\*args, \*\*kwargs) | Sets the categories to the specified new_categories. |
Series.cat.as_ordered(\*args, \*\*kwargs) | Sets the Categorical to be ordered |
Series.cat.as_unordered(\*args, \*\*kwargs) | Sets the Categorical to be unordered |
To create a Series of dtypecategory, usecat=s.astype("category").
The following twoCategorical constructors are considered API but should only be used whenadding 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 |
Categorical.from_codes(codes, categories[, ...]) | Make a Categorical type from codes and categories arrays. |
np.asarray(categorical) works by implementing the array interface. Be aware, that this convertsthe Categorical back to a numpy array, so levels and order information is not preserved!
Categorical.__array__([dtype]) | The numpy array interface. |
Series.plot is both a callable method and a namespace attribute forspecific plotting methods of the formSeries.plot.<kind>.
Series.plot([kind, ax, figsize, ....]) | Series plotting accessor and method |
Series.plot.area(\*\*kwds) | Area plot |
Series.plot.bar(\*\*kwds) | Vertical bar plot |
Series.plot.barh(\*\*kwds) | Horizontal bar plot |
Series.plot.box(\*\*kwds) | Boxplot |
Series.plot.density(\*\*kwds) | Kernel Density Estimate plot |
Series.plot.hist([bins]) | Histogram |
Series.plot.kde(\*\*kwds) | Kernel Density Estimate plot |
Series.plot.line(\*\*kwds) | Line plot |
Series.plot.pie(\*\*kwds) | Pie chart |
Series.hist([by, ax, grid, xlabelsize, ...]) | Draw histogram of the input series using matplotlib |
Series.from_csv(path[, sep, parse_dates, ...]) | Read CSV file (DISCOURAGED, please usepandas.read_csv() instead). |
Series.to_pickle(path) | 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_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. |
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([data, index, columns, dtype, copy]) | Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). |
Axes
- index: row labels
- columns: column labels
DataFrame.as_matrix([columns]) | Convert the frame to its Numpy-array representation. |
DataFrame.dtypes | Return the dtypes in this object. |
DataFrame.ftypes | Return the ftypes (indication of sparse/dense and dtype) in this object. |
DataFrame.get_dtype_counts() | Return the counts of dtypes in this object. |
DataFrame.get_ftype_counts() | Return the counts of ftypes in this object. |
DataFrame.select_dtypes([include, exclude]) | Return a subset of a DataFrame including/excluding columns based on theirdtype. |
DataFrame.values | Numpy representation of NDFrame |
DataFrame.axes | Return a list with the row axis labels and column axis labels as the only members. |
DataFrame.ndim | Number of axes / array dimensions |
DataFrame.size | number of elements in the NDFrame |
DataFrame.shape | Return a tuple representing the dimensionality of the DataFrame. |
DataFrame.memory_usage([index, deep]) | Memory usage of DataFrame columns. |
DataFrame.astype(dtype[, copy, raise_on_error]) | Cast object to input numpy.dtype |
DataFrame.convert_objects([convert_dates, ...]) | Deprecated. |
DataFrame.copy([deep]) | Make a copy of this objects data. |
DataFrame.isnull() | Return a boolean same-sized object indicating if the values are null. |
DataFrame.notnull() | Return a boolean same-sized object indicating if the values are not null. |
DataFrame.head([n]) | Returns first n rows |
DataFrame.at | Fast label-based scalar accessor |
DataFrame.iat | Fast integer location scalar accessor. |
DataFrame.ix | A primarily label-location based indexer, with integer position fallback. |
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,.ix,.loc, and.iloc, see theindexing documentation.
DataFrame.add(other[, axis, level, fill_value]) | Addition of dataframe and other, element-wise (binary operatoradd). |
DataFrame.sub(other[, axis, level, fill_value]) | Subtraction of dataframe and other, element-wise (binary operatorsub). |
DataFrame.mul(other[, axis, level, fill_value]) | Multiplication of dataframe and other, element-wise (binary operatormul). |
DataFrame.div(other[, axis, level, fill_value]) | Floating division of dataframe and other, element-wise (binary operatortruediv). |
DataFrame.truediv(other[, axis, level, ...]) | Floating division of dataframe and other, element-wise (binary operatortruediv). |
DataFrame.floordiv(other[, axis, level, ...]) | Integer division of dataframe and other, element-wise (binary operatorfloordiv). |
DataFrame.mod(other[, axis, level, fill_value]) | Modulo of dataframe and other, element-wise (binary operatormod). |
DataFrame.pow(other[, axis, level, fill_value]) | Exponential power of dataframe and other, element-wise (binary operatorpow). |
DataFrame.radd(other[, axis, level, fill_value]) | Addition of dataframe and other, element-wise (binary operatorradd). |
DataFrame.rsub(other[, axis, level, fill_value]) | Subtraction of dataframe and other, element-wise (binary operatorrsub). |
DataFrame.rmul(other[, axis, level, fill_value]) | Multiplication of dataframe and other, element-wise (binary operatorrmul). |
DataFrame.rdiv(other[, axis, level, fill_value]) | Floating division of dataframe and other, element-wise (binary operatorrtruediv). |
DataFrame.rtruediv(other[, axis, level, ...]) | Floating division of dataframe and other, element-wise (binary operatorrtruediv). |
DataFrame.rfloordiv(other[, axis, level, ...]) | Integer division of dataframe and other, element-wise (binary operatorrfloordiv). |
DataFrame.rmod(other[, axis, level, fill_value]) | Modulo of dataframe and other, element-wise (binary operatorrmod). |
DataFrame.rpow(other[, axis, level, fill_value]) | Exponential power of dataframe and other, element-wise (binary operatorrpow). |
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. |
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.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 |
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, ...]) | Generate various summary statistics, 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. |
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(\*args, \*\*kwargs) | Return DataFrame with duplicate rows removed, optionally only |
DataFrame.duplicated(\*args, \*\*kwargs) | 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. |
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’. |
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.sortlevel([level, axis, ...]) | Sort multilevel index by chosen axis and primary level. |
DataFrame.nlargest(n, columns[, keep]) | Get the rows of a DataFrame sorted by then largest values ofcolumns. |
DataFrame.nsmallest(n, columns[, keep]) | Get the rows of a DataFrame sorted by then smallest values ofcolumns. |
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.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 |
DataFrame.append(other[, ignore_index, ...]) | Append rows ofother 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. |
DataFrame.asfreq(freq[, method, how, normalize]) | 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, atbeginning of period |
DataFrame.tz_convert(tz[, axis, level, copy]) | Convert tz-aware axis to target time zone. |
DataFrame.tz_localize(\*args, \*\*kwargs) | Localize tz-naive TimeSeries to target time zone. |
DataFrame.plot is both a callable method and a namespace attribute forspecific plotting methods of the formDataFrame.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. |
DataFrame.from_csv(path[, header, sep, ...]) | Read CSV file (DISCOURAGED, please usepandas.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) | 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 a 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_latex([buf, columns, ...]) | Render a DataFrame 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([data, items, major_axis, minor_axis, ...]) | Represents wide format panel data, stored as 3-dimensional array |
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 | Numpy representation of NDFrame |
Panel.axes | Return index label(s) of the internal NDFrame |
Panel.ndim | Number of axes / array dimensions |
Panel.size | number of elements in the NDFrame |
Panel.shape | Return a tuple of axis dimensions |
Panel.dtypes | Return the dtypes in this object. |
Panel.ftypes | Return the ftypes (indication of sparse/dense and dtype) in this object. |
Panel.get_dtype_counts() | Return the counts of dtypes in this object. |
Panel.get_ftype_counts() | Return the counts of ftypes in this object. |
Panel.astype(dtype[, copy, raise_on_error]) | 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. |
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 |
Panel.at | Fast label-based scalar accessor |
Panel.iat | Fast integer location scalar accessor. |
Panel.ix | A primarily label-location based indexer, with integer position fallback. |
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,.ix,.loc, and.iloc, see theindexing documentation.
Panel.add(other[, axis]) | Addition of series and other, element-wise (binary operatoradd). |
Panel.sub(other[, axis]) | Subtraction of series and other, element-wise (binary operatorsub). |
Panel.mul(other[, axis]) | Multiplication of series and other, element-wise (binary operatormul). |
Panel.div(other[, axis]) | Floating division of series and other, element-wise (binary operatortruediv). |
Panel.truediv(other[, axis]) | Floating division of series and other, element-wise (binary operatortruediv). |
Panel.floordiv(other[, axis]) | Integer division of series and other, element-wise (binary operatorfloordiv). |
Panel.mod(other[, axis]) | Modulo of series and other, element-wise (binary operatormod). |
Panel.pow(other[, axis]) | Exponential power of series and other, element-wise (binary operatorpow). |
Panel.radd(other[, axis]) | Addition of series and other, element-wise (binary operatorradd). |
Panel.rsub(other[, axis]) | Subtraction of series and other, element-wise (binary operatorrsub). |
Panel.rmul(other[, axis]) | Multiplication of series and other, element-wise (binary operatorrmul). |
Panel.rdiv(other[, axis]) | Floating division of series and other, element-wise (binary operatorrtruediv). |
Panel.rtruediv(other[, axis]) | Floating division of series and other, element-wise (binary operatorrtruediv). |
Panel.rfloordiv(other[, axis]) | Integer division of series and other, element-wise (binary operatorrfloordiv). |
Panel.rmod(other[, axis]) | Modulo of series and other, element-wise (binary operatorrmod). |
Panel.rpow(other[, axis]) | Exponential power of series and other, element-wise (binary operatorrpow). |
Panel.lt(other[, axis]) | Wrapper for comparison method lt |
Panel.gt(other[, axis]) | Wrapper for comparison method gt |
Panel.le(other[, axis]) | Wrapper for comparison method le |
Panel.ge(other[, axis]) | Wrapper for comparison method ge |
Panel.ne(other[, axis]) | Wrapper for comparison method ne |
Panel.eq(other[, axis]) | Wrapper for comparison method eq |
Panel.apply(func[, axis]) | Applies function along axis (or axes) of the Panel |
Panel.groupby(function[, axis]) | Group data on given axis, returning GroupBy object |
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. |
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. |
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 |
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. |
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. |
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(\*args, \*\*kwargs) | Localize tz-naive TimeSeries to target time zone. |
Panel.from_dict(data[, intersect, orient, dtype]) | Construct Panel from dict of DataFrame objects |
Panel.to_pickle(path) | 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. |
Panel4D([data, labels, items, major_axis, ...]) | Panel4D is a 4-Dimensional named container very much like a Panel, but having 4 named dimensions. |
Panel4D.to_xarray() | Return an xarray object from the pandas object. |
Axes
- labels: axis 1; each label corresponds to a Panel contained inside
- items: axis 2; each item corresponds to a DataFrame contained inside
- major_axis: axis 3; the index (rows) of each of the DataFrames
- minor_axis: axis 4; the columns of each of the DataFrames
Panel4D.values | Numpy representation of NDFrame |
Panel4D.axes | Return index label(s) of the internal NDFrame |
Panel4D.ndim | Number of axes / array dimensions |
Panel4D.size | number of elements in the NDFrame |
Panel4D.shape | Return a tuple of axis dimensions |
Panel4D.dtypes | Return the dtypes in this object. |
Panel4D.ftypes | Return the ftypes (indication of sparse/dense and dtype) in this object. |
Panel4D.get_dtype_counts() | Return the counts of dtypes in this object. |
Panel4D.get_ftype_counts() | Return the counts of ftypes in this object. |
Panel4D.astype(dtype[, copy, raise_on_error]) | Cast object to input numpy.dtype |
Panel4D.copy([deep]) | Make a copy of this objects data. |
Panel4D.isnull() | Return a boolean same-sized object indicating if the values are null. |
Panel4D.notnull() | Return a boolean same-sized object indicating if the values are not null. |
Many of these methods or variants thereof are available on the objectsthat contain an index (Series/Dataframe) and those should most likely beused before calling these methods directly.
Index | Immutable ndarray implementing an ordered, sliceable set. |
Index.values | return the underlying data as an ndarray |
Index.is_monotonic | alias for is_monotonic_increasing (deprecated) |
Index.is_monotonic_increasing | return if the index is monotonic increasing (only equal or |
Index.is_monotonic_decreasing | return if the index is monotonic decreasing (only equal or |
Index.is_unique | |
Index.has_duplicates | |
Index.dtype | |
Index.inferred_type | |
Index.is_all_dates | |
Index.shape | return a tuple of the shape of the underlying data |
Index.nbytes | return the number of bytes in the underlying data |
Index.ndim | return the number of dimensions of the underlying data, |
Index.size | return the number of elements in the underlying data |
Index.strides | return the strides of the underlying data |
Index.itemsize | return the size of the dtype of the item of the underlying data |
Index.base | return the base object if the memory of the underlying data is |
Index.T | return the transpose, which is by definition self |
Index.memory_usage([deep]) | Memory usage of my values |
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(\*args, \*\*kwargs) | Return Index with duplicate values removed |
Index.duplicated(\*args, \*\*kwargs) | 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(n, \*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 %(klass)s 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 Index of 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. |
Index.fillna([value, downcast]) | Fill NA/NaN values with the specified value |
Index.dropna([how]) | Return Index without NA/NaN values |
Index.astype(dtype[, copy]) | Create an Index with values cast to dtypes. |
Index.tolist() | return a list of the Index values |
Index.to_datetime([dayfirst]) | DEPRECATED: usepandas.to_datetime() instead. |
Index.to_series(\*\*kwargs) | Create a Series with both index and values equal to the index keys |
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 |
Index.shift([periods, freq]) | Shift Index containing datetime objects by input number of periods and |
Index.append(other) | Append a collection of Index options together |
Index.join(other[, how, level, return_indexers]) | 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 inother. |
Index.symmetric_difference(other[, result_name]) | Compute the symmetric difference of two Index objects. |
Index.get_indexer(target[, method, limit, ...]) | Compute indexer and mask for new index given the current index. |
Index.get_indexer_non_unique(target) | return an indexer suitable for taking from a non unique index |
Index.get_level_values(level) | Return vector of label 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. |
CategoricalIndex | Immutable Index implementing an ordered, sliceable set. |
CategoricalIndex.codes | |
CategoricalIndex.categories | |
CategoricalIndex.ordered | |
CategoricalIndex.rename_categories(\*args, ...) | Renames categories. |
CategoricalIndex.reorder_categories(\*args, ...) | Reorders categories as specified in new_categories. |
CategoricalIndex.add_categories(\*args, \*\*kwargs) | Add new categories. |
CategoricalIndex.remove_categories(\*args, ...) | Removes the specified categories. |
CategoricalIndex.remove_unused_categories(...) | Removes categories which are not used. |
CategoricalIndex.set_categories(\*args, \*\*kwargs) | Sets the categories to the specified new_categories. |
CategoricalIndex.as_ordered(\*args, \*\*kwargs) | Sets the Categorical to be ordered |
CategoricalIndex.as_unordered(\*args, \*\*kwargs) | Sets the Categorical to be unordered |
MultiIndex | A multi-level, or hierarchical, index object for pandas objects |
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.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. |
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. |
DatetimeIndex.year | The year of the datetime |
DatetimeIndex.month | The month as January=1, December=12 |
DatetimeIndex.day | The days of the datetime |
DatetimeIndex.hour | The hours of the datetime |
DatetimeIndex.minute | The minutes of the datetime |
DatetimeIndex.second | The seconds of the datetime |
DatetimeIndex.microsecond | The microseconds of the datetime |
DatetimeIndex.nanosecond | The nanoseconds of the datetime |
DatetimeIndex.date | Returns numpy array of python datetime.date objects (namely, the date part of Timestamps without timezone information). |
DatetimeIndex.time | Returns numpy array of datetime.time. |
DatetimeIndex.dayofyear | The ordinal day of the year |
DatetimeIndex.weekofyear | The week ordinal of the year |
DatetimeIndex.week | The week ordinal of the year |
DatetimeIndex.dayofweek | The day of the week with Monday=0, Sunday=6 |
DatetimeIndex.weekday | The day of the week with Monday=0, Sunday=6 |
DatetimeIndex.weekday_name | The name of day in a week (ex: Friday) |
DatetimeIndex.quarter | The quarter of the date |
DatetimeIndex.tz | |
DatetimeIndex.freq | get/set the frequncy of the Index |
DatetimeIndex.freqstr | Return the frequency object as a string if its set, otherwise None |
DatetimeIndex.is_month_start | Logical indicating if first day of month (defined by frequency) |
DatetimeIndex.is_month_end | Logical indicating if last day of month (defined by frequency) |
DatetimeIndex.is_quarter_start | Logical indicating if first day of quarter (defined by frequency) |
DatetimeIndex.is_quarter_end | Logical indicating if last day of quarter (defined by frequency) |
DatetimeIndex.is_year_start | Logical indicating if first day of year (defined by frequency) |
DatetimeIndex.is_year_end | Logical indicating if last day of year (defined by frequency) |
DatetimeIndex.is_leap_year | Logical indicating if the date belongs to a leap year |
DatetimeIndex.inferred_freq |
DatetimeIndex.indexer_at_time(time[, asof]) | Select values at particular time of day (e.g. |
DatetimeIndex.indexer_between_time(...[, ...]) | Select values between particular times of day (e.g., 9:00-9:30AM). |
DatetimeIndex.normalize() | Return DatetimeIndex with times to midnight. |
DatetimeIndex.strftime(date_format) | Return an array of formatted strings specified by date_format, which supports the same string format as the python standard library. |
DatetimeIndex.snap([freq]) | Snap time stamps to nearest occurring frequency |
DatetimeIndex.tz_convert(tz) | Convert tz-aware DatetimeIndex from one time zone to another (using |
DatetimeIndex.tz_localize(\*args, \*\*kwargs) | Localize tz-naive DatetimeIndex to given time zone (using |
DatetimeIndex.round(freq, \*args, \*\*kwargs) | round the index to the specified freq |
DatetimeIndex.floor(freq) | floor the index to the specified freq |
DatetimeIndex.ceil(freq) | ceil the index to the specified freq |
DatetimeIndex.to_datetime([dayfirst]) | |
DatetimeIndex.to_period([freq]) | Cast to PeriodIndex at a particular frequency |
DatetimeIndex.to_perioddelta(freq) | Calcuates TimedeltaIndex of difference between index values and index converted to PeriodIndex at specified freq. |
DatetimeIndex.to_pydatetime() | Return DatetimeIndex as object ndarray of datetime.datetime objects |
DatetimeIndex.to_series([keep_tz]) | Create a Series with both index and values equal to the index keys |
TimedeltaIndex | Immutable ndarray of timedelta64 data, represented internally as int64, and |
TimedeltaIndex.days | Number of days for each element. |
TimedeltaIndex.seconds | Number of seconds (>= 0 and less than 1 day) for each element. |
TimedeltaIndex.microseconds | Number of microseconds (>= 0 and less than 1 second) for each element. |
TimedeltaIndex.nanoseconds | Number of nanoseconds (>= 0 and less than 1 microsecond) for each element. |
TimedeltaIndex.components | Return a dataframe of the components (days, hours, minutes, seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas. |
TimedeltaIndex.inferred_freq |
TimedeltaIndex.to_pytimedelta() | Return TimedeltaIndex as object ndarray of datetime.timedelta objects |
TimedeltaIndex.to_series(\*\*kwargs) | Create a Series with both index and values equal to the index keys |
TimedeltaIndex.round(freq, \*args, \*\*kwargs) | round the index to the specified freq |
TimedeltaIndex.floor(freq) | floor the index to the specified freq |
TimedeltaIndex.ceil(freq) | ceil the index to the specified freq |
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 |
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 |
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 objects are returned by groupby calls:pandas.DataFrame.groupby(),pandas.Series.groupby(), etc.
GroupBy.__iter__() | Groupby iterator |
GroupBy.groups | dict {group name -> group labels} |
GroupBy.indices | dict {group name -> group indices} |
GroupBy.get_group(name[, obj]) | Constructs NDFrame from group with provided name |
Grouper([key, level, freq, axis, sort]) | A Grouper allows the user to specify a groupby instruction for a target |
GroupBy.apply(func, \*args, \*\*kwargs) | Apply function and combine results together in an intelligent way. |
GroupBy.aggregate(func, \*args, \*\*kwargs) | |
GroupBy.transform(func, \*args, \*\*kwargs) |
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() | Compute first of group values |
GroupBy.head([n]) | Returns first n rows of each group. |
GroupBy.last() | Compute last of group values |
GroupBy.max() | Compute max of group values |
GroupBy.mean(\*args, \*\*kwargs) | Compute mean of groups, excluding missing values |
GroupBy.median() | Compute median of groups, excluding missing values |
GroupBy.min() | Compute min of group values |
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() | 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() | 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 bothSeriesGroupBy andDataFrameGroupBy objects, but may differ slightly, usually in thattheDataFrameGroupBy version usually permits the specification of anaxis argument, and often an argument indicating whether to restrictapplication to columns of a specific data type.
DataFrameGroupBy.agg(arg, \*args, \*\*kwargs) | Aggregate using input function or dict of {column -> |
DataFrameGroupBy.all([axis, bool_only, ...]) | Return whether all elements are True over requested axis |
DataFrameGroupBy.any([axis, bool_only, ...]) | Return whether any element is True over requested axis |
DataFrameGroupBy.bfill([limit]) | Backward fill the values |
DataFrameGroupBy.corr([method, min_periods]) | Compute pairwise correlation of columns, excluding NA/null values |
DataFrameGroupBy.count() | Compute count of group, excluding missing values |
DataFrameGroupBy.cov([min_periods]) | Compute pairwise covariance of columns, excluding NA/null values |
DataFrameGroupBy.cummax([axis, skipna]) | Return cumulative max over requested axis. |
DataFrameGroupBy.cummin([axis, skipna]) | Return cumulative minimum over requested axis. |
DataFrameGroupBy.cumprod([axis]) | Cumulative product for each group |
DataFrameGroupBy.cumsum([axis]) | Cumulative sum for each group |
DataFrameGroupBy.describe([percentiles, ...]) | Generate various summary statistics, excluding NaN values. |
DataFrameGroupBy.diff([periods, axis]) | 1st discrete difference of object |
DataFrameGroupBy.ffill([limit]) | Forward fill the values |
DataFrameGroupBy.fillna([value, method, ...]) | Fill NA/NaN values using the specified method |
DataFrameGroupBy.hist(data[, column, by, ...]) | Draw histogram of the DataFrame’s series using matplotlib / pylab. |
DataFrameGroupBy.idxmax([axis, skipna]) | Return index of first occurrence of maximum over requested axis. |
DataFrameGroupBy.idxmin([axis, skipna]) | Return index of first occurrence of minimum over requested axis. |
DataFrameGroupBy.mad([axis, skipna, level]) | Return the mean absolute deviation of the values for the requested axis |
DataFrameGroupBy.pct_change([periods, ...]) | Percent change over given number of periods. |
DataFrameGroupBy.plot | Class implementing the .plot attribute for groupby objects |
DataFrameGroupBy.quantile([q, axis, ...]) | Return values at the given quantile over requested axis, a la numpy.percentile. |
DataFrameGroupBy.rank([axis, method, ...]) | 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([axis, skipna, level, ...]) | Return unbiased skew over requested axis |
DataFrameGroupBy.take(indices[, axis, ...]) | Analogous to ndarray.take |
DataFrameGroupBy.tshift([periods, freq, axis]) | Shift the time index, using the index’s frequency if available. |
The following methods are available only forSeriesGroupBy objects.
SeriesGroupBy.nlargest(\*args, \*\*kwargs) | Return the largestn elements. |
SeriesGroupBy.nsmallest(\*args, \*\*kwargs) | Return the smallestn elements. |
SeriesGroupBy.nunique([dropna]) | Returns number of unique elements in the group |
SeriesGroupBy.unique() | Return np.ndarray of unique values in the object. |
SeriesGroupBy.value_counts([normalize, ...]) |
The following methods are available only forDataFrameGroupBy objects.
DataFrameGroupBy.corrwith(other[, axis, drop]) | Compute pairwise correlation between rows or columns of two DataFrame objects. |
DataFrameGroupBy.boxplot(grouped[, ...]) | Make box plots from DataFrameGroupBy data. |
Resampler objects are returned by resample calls:pandas.DataFrame.resample(),pandas.Series.resample().
Resampler.__iter__() | Groupby iterator |
Resampler.groups | dict {group name -> group labels} |
Resampler.indices | dict {group name -> group indices} |
Resampler.get_group(name[, obj]) | Constructs NDFrame from group with provided name |
Resampler.apply(arg, \*args, \*\*kwargs) | Apply aggregation function or functions to resampled groups, yielding |
Resampler.aggregate(arg, \*args, \*\*kwargs) | Apply aggregation function or functions to resampled groups, yielding |
Resampler.transform(arg, \*args, \*\*kwargs) | Call function producing a like-indexed Series on each group and return |
Resampler.ffill([limit]) | Forward fill the values |
Resampler.backfill([limit]) | Backward fill the values |
Resampler.bfill([limit]) | Backward fill the values |
Resampler.pad([limit]) | Forward fill the values |
Resampler.fillna(method[, limit]) | Fill missing values |
Resampler.asfreq() | return the values at the new freq, |
Resampler.interpolate([method, axis, limit, ...]) | Interpolate values according to different methods. |
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 |
Resampler.prod([_method]) | Compute prod of group values |
Resampler.size([_method]) | 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]) | Compute sum of group values |
Resampler.var([ddof]) | Compute variance of groups, excluding missing values |
Styler objects are returned bypandas.DataFrame.style.
Styler(data[, precision, table_styles, ...]) | Helps style a DataFrame or Series according to the data with HTML and CSS. |
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. |
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 backgroundnull_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]) | Color the backgroundcolor proptional to the values in each column. |
Styler.render() | 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 fromStyler.export. |
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 thewith statement context. |