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pandas.DataFrame.between_time DataFrame.between_time(start_time, end_time, include_start=NoDefault.no_default, include_end=NoDefault.no_default, inclusive=None, axis=None)[source]
Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting start_time to be later than end_time, you can get the... | pandas.reference.api.pandas.dataframe.between_time |
pandas.DataFrame.bfill DataFrame.bfill(axis=None, inplace=False, limit=None, downcast=None)[source]
Synonym for DataFrame.fillna() with method='bfill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | pandas.reference.api.pandas.dataframe.bfill |
pandas.DataFrame.bool DataFrame.bool()[source]
Return the bool of a single element Series or DataFrame. This must be a boolean scalar value, either True or False. It will raise a ValueError if the Series or DataFrame does not have exactly 1 element, or that element is not boolean (integer values 0 and 1 will also r... | pandas.reference.api.pandas.dataframe.bool |
pandas.DataFrame.boxplot DataFrame.boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, backend=None, **kwargs)[source]
Make a box plot from DataFrame columns. Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns... | pandas.reference.api.pandas.dataframe.boxplot |
pandas.DataFrame.clip DataFrame.clip(lower=None, upper=None, axis=None, inplace=False, *args, **kwargs)[source]
Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the... | pandas.reference.api.pandas.dataframe.clip |
pandas.DataFrame.columns DataFrame.columns
The column labels of the DataFrame. | pandas.reference.api.pandas.dataframe.columns |
pandas.DataFrame.combine DataFrame.combine(other, func, fill_value=None, overwrite=True)[source]
Perform column-wise combine with another DataFrame. Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the tw... | pandas.reference.api.pandas.dataframe.combine |
pandas.DataFrame.combine_first DataFrame.combine_first(other)[source]
Update null elements with value in the same location in other. Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the unio... | pandas.reference.api.pandas.dataframe.combine_first |
pandas.DataFrame.compare DataFrame.compare(other, align_axis=1, keep_shape=False, keep_equal=False)[source]
Compare to another DataFrame and show the differences. New in version 1.1.0. Parameters
other:DataFrame
Object to compare with.
align_axis:{0 or ‘index’, 1 or ‘columns’}, default 1
Determine which... | pandas.reference.api.pandas.dataframe.compare |
pandas.DataFrame.convert_dtypes DataFrame.convert_dtypes(infer_objects=True, convert_string=True, convert_integer=True, convert_boolean=True, convert_floating=True)[source]
Convert columns to best possible dtypes using dtypes supporting pd.NA. New in version 1.0.0. Parameters
infer_objects:bool, default True
... | pandas.reference.api.pandas.dataframe.convert_dtypes |
pandas.DataFrame.copy DataFrame.copy(deep=True)[source]
Make a copy of this object’s indices and data. When deep=True (default), a new object will be created with a copy of the calling object’s data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes... | pandas.reference.api.pandas.dataframe.copy |
pandas.DataFrame.corr DataFrame.corr(method='pearson', min_periods=1)[source]
Compute pairwise correlation of columns, excluding NA/null values. Parameters
method:{‘pearson’, ‘kendall’, ‘spearman’} or callable
Method of correlation: pearson : standard correlation coefficient kendall : Kendall Tau correlation... | pandas.reference.api.pandas.dataframe.corr |
pandas.DataFrame.corrwith DataFrame.corrwith(other, axis=0, drop=False, method='pearson')[source]
Compute pairwise correlation. Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correla... | pandas.reference.api.pandas.dataframe.corrwith |
pandas.DataFrame.count DataFrame.count(axis=0, level=None, numeric_only=False)[source]
Count non-NA cells for each column or row. The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
If... | pandas.reference.api.pandas.dataframe.count |
pandas.DataFrame.cov DataFrame.cov(min_periods=None, ddof=1)[source]
Compute pairwise covariance of columns, excluding NA/null values. Compute the pairwise covariance among the series of a DataFrame. The returned data frame is the covariance matrix of the columns of the DataFrame. Both NA and null values are automa... | pandas.reference.api.pandas.dataframe.cov |
pandas.DataFrame.cummax DataFrame.cummax(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative maximum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative maximum. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name... | pandas.reference.api.pandas.dataframe.cummax |
pandas.DataFrame.cummin DataFrame.cummin(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative minimum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative minimum. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name... | pandas.reference.api.pandas.dataframe.cummin |
pandas.DataFrame.cumprod DataFrame.cumprod(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative product over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative product. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the na... | pandas.reference.api.pandas.dataframe.cumprod |
pandas.DataFrame.cumsum DataFrame.cumsum(axis=None, skipna=True, *args, **kwargs)[source]
Return cumulative sum over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative sum. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The index or the name of the ... | pandas.reference.api.pandas.dataframe.cumsum |
pandas.DataFrame.describe DataFrame.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)[source]
Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values. Analyzes bo... | pandas.reference.api.pandas.dataframe.describe |
pandas.DataFrame.diff DataFrame.diff(periods=1, axis=0)[source]
First discrete difference of element. Calculates the difference of a Dataframe element compared with another element in the Dataframe (default is element in previous row). Parameters
periods:int, default 1
Periods to shift for calculating differe... | pandas.reference.api.pandas.dataframe.diff |
pandas.DataFrame.div DataFrame.div(other, axis='columns', level=None, fill_value=None)[source]
Get Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse versio... | pandas.reference.api.pandas.dataframe.div |
pandas.DataFrame.divide DataFrame.divide(other, axis='columns', level=None, fill_value=None)[source]
Get Floating division of dataframe and other, element-wise (binary operator truediv). Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse ... | pandas.reference.api.pandas.dataframe.divide |
pandas.DataFrame.dot DataFrame.dot(other)[source]
Compute the matrix multiplication between the DataFrame and other. This method computes the matrix product between the DataFrame and the values of an other Series, DataFrame or a numpy array. It can also be called using self @ other in Python >= 3.5. Parameters
... | pandas.reference.api.pandas.dataframe.dot |
pandas.DataFrame.drop DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]
Drop specified labels from rows or columns. Remove rows or columns by specifying label names and corresponding axis, or by specifying directly index or column names. When using a mu... | pandas.reference.api.pandas.dataframe.drop |
pandas.DataFrame.drop_duplicates DataFrame.drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)[source]
Return DataFrame with duplicate rows removed. Considering certain columns is optional. Indexes, including time indexes are ignored. Parameters
subset:column label or sequence of label... | pandas.reference.api.pandas.dataframe.drop_duplicates |
pandas.DataFrame.droplevel DataFrame.droplevel(level, axis=0)[source]
Return Series/DataFrame with requested index / column level(s) removed. Parameters
level:int, str, or list-like
If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
axis:{... | pandas.reference.api.pandas.dataframe.droplevel |
pandas.DataFrame.dropna DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)[source]
Remove missing values. See the User Guide for more on which values are considered missing, and how to work with missing data. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
Determine if rows... | pandas.reference.api.pandas.dataframe.dropna |
pandas.DataFrame.dtypes propertyDataFrame.dtypes
Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result’s index is the original DataFrame’s columns. Columns with mixed types are stored with the object dtype. See the User Guide for more. Returns
pandas.Series
The ... | pandas.reference.api.pandas.dataframe.dtypes |
pandas.DataFrame.duplicated DataFrame.duplicated(subset=None, keep='first')[source]
Return boolean Series denoting duplicate rows. Considering certain columns is optional. Parameters
subset:column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by default use al... | pandas.reference.api.pandas.dataframe.duplicated |
pandas.DataFrame.empty propertyDataFrame.empty
Indicator whether Series/DataFrame is empty. True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns
bool
If Series/DataFrame is empty, return True, if not return False. See also Series.dropna
Return series wit... | pandas.reference.api.pandas.dataframe.empty |
pandas.DataFrame.eq DataFrame.eq(other, axis='columns', level=None)[source]
Get Equal to of dataframe and other, element-wise (binary operator eq). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for... | pandas.reference.api.pandas.dataframe.eq |
pandas.DataFrame.equals DataFrame.equals(other)[source]
Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The row/column index do not nee... | pandas.reference.api.pandas.dataframe.equals |
pandas.DataFrame.eval DataFrame.eval(expr, inplace=False, **kwargs)[source]
Evaluate a string describing operations on DataFrame columns. Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this fu... | pandas.reference.api.pandas.dataframe.eval |
pandas.DataFrame.ewm DataFrame.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, times=None, method='single')[source]
Provide exponentially weighted (EW) calculations. Exactly one parameter: com, span, halflife, or alpha must be provided. Parameters
com:flo... | pandas.reference.api.pandas.dataframe.ewm |
pandas.DataFrame.expanding DataFrame.expanding(min_periods=1, center=None, axis=0, method='single')[source]
Provide expanding window calculations. Parameters
min_periods:int, default 1
Minimum number of observations in window required to have a value; otherwise, result is np.nan.
center:bool, default False
... | pandas.reference.api.pandas.dataframe.expanding |
pandas.DataFrame.explode DataFrame.explode(column, ignore_index=False)[source]
Transform each element of a list-like to a row, replicating index values. New in version 0.25.0. Parameters
column:IndexLabel
Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple,... | pandas.reference.api.pandas.dataframe.explode |
pandas.DataFrame.ffill DataFrame.ffill(axis=None, inplace=False, limit=None, downcast=None)[source]
Synonym for DataFrame.fillna() with method='ffill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | pandas.reference.api.pandas.dataframe.ffill |
pandas.DataFrame.fillna DataFrame.fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None)[source]
Fill NA/NaN values using the specified method. Parameters
value:scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values s... | pandas.reference.api.pandas.dataframe.fillna |
pandas.DataFrame.filter DataFrame.filter(items=None, like=None, regex=None, axis=None)[source]
Subset the dataframe rows or columns according to the specified index labels. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters
items:list... | pandas.reference.api.pandas.dataframe.filter |
pandas.DataFrame.first DataFrame.first(offset)[source]
Select initial periods of time series data based on a date offset. When having a DataFrame with dates as index, this function can select the first few rows based on a date offset. Parameters
offset:str, DateOffset or dateutil.relativedelta
The offset leng... | pandas.reference.api.pandas.dataframe.first |
pandas.DataFrame.first_valid_index DataFrame.first_valid_index()[source]
Return index for first non-NA value or None, if no NA value is found. Returns
scalar:type of index
Notes If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame. | pandas.reference.api.pandas.dataframe.first_valid_index |
pandas.DataFrame.flags propertyDataFrame.flags
Get the properties associated with this pandas object. The available flags are Flags.allows_duplicate_labels See also Flags
Flags that apply to pandas objects. DataFrame.attrs
Global metadata applying to this dataset. Notes “Flags” differ from “metadata”. Fla... | pandas.reference.api.pandas.dataframe.flags |
pandas.DataFrame.floordiv DataFrame.floordiv(other, axis='columns', level=None, fill_value=None)[source]
Get Integer division of dataframe and other, element-wise (binary operator floordiv). Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs. With rev... | pandas.reference.api.pandas.dataframe.floordiv |
pandas.DataFrame.from_dict classmethodDataFrame.from_dict(data, orient='columns', dtype=None, columns=None)[source]
Construct DataFrame from dict of array-like or dicts. Creates DataFrame object from dictionary by columns or by index allowing dtype specification. Parameters
data:dict
Of the form {field : arra... | pandas.reference.api.pandas.dataframe.from_dict |
pandas.DataFrame.from_records classmethodDataFrame.from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)[source]
Convert structured or record ndarray to DataFrame. Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame. Parameters
dat... | pandas.reference.api.pandas.dataframe.from_records |
pandas.DataFrame.ge DataFrame.ge(other, axis='columns', level=None)[source]
Get Greater than or equal to of dataframe and other, element-wise (binary operator ge). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or column... | pandas.reference.api.pandas.dataframe.ge |
pandas.DataFrame.get DataFrame.get(key, default=None)[source]
Get item from object for given key (ex: DataFrame column). Returns default value if not found. Parameters
key:object
Returns
value:same type as items contained in object
Examples
>>> df = pd.DataFrame(
... [
... [24.3, 75.7, "h... | pandas.reference.api.pandas.dataframe.get |
pandas.DataFrame.groupby DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=NoDefault.no_default, observed=False, dropna=True)[source]
Group DataFrame using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, apply... | pandas.reference.api.pandas.dataframe.groupby |
pandas.DataFrame.gt DataFrame.gt(other, axis='columns', level=None)[source]
Get Greater than of dataframe and other, element-wise (binary operator gt). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level... | pandas.reference.api.pandas.dataframe.gt |
pandas.DataFrame.head DataFrame.head(n=5)[source]
Return the first n rows. This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it. For negative values of n, this function returns all rows except the last n rows, equiv... | pandas.reference.api.pandas.dataframe.head |
pandas.DataFrame.hist DataFrame.hist(column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, backend=None, legend=False, **kwargs)[source]
Make a histogram of the DataFrame’s columns. A histogram is a represent... | pandas.reference.api.pandas.dataframe.hist |
pandas.DataFrame.iat propertyDataFrame.iat
Access a single value for a row/column pair by integer position. Similar to iloc, in that both provide integer-based lookups. Use iat if you only need to get or set a single value in a DataFrame or Series. Raises
IndexError
When integer position is out of bounds. ... | pandas.reference.api.pandas.dataframe.iat |
pandas.DataFrame.idxmax DataFrame.idxmax(axis=0, skipna=True)[source]
Return index of first occurrence of maximum over requested axis. NA/null values are excluded. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna:boo... | pandas.reference.api.pandas.dataframe.idxmax |
pandas.DataFrame.idxmin DataFrame.idxmin(axis=0, skipna=True)[source]
Return index of first occurrence of minimum over requested axis. NA/null values are excluded. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to use. 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
skipna:boo... | pandas.reference.api.pandas.dataframe.idxmin |
pandas.DataFrame.iloc propertyDataFrame.iloc
Purely integer-location based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]... | pandas.reference.api.pandas.dataframe.iloc |
pandas.DataFrame.index DataFrame.index
The index (row labels) of the DataFrame. | pandas.reference.api.pandas.dataframe.index |
pandas.DataFrame.infer_objects DataFrame.infer_objects()[source]
Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. Returns
... | pandas.reference.api.pandas.dataframe.infer_objects |
pandas.DataFrame.info DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None)[source]
Print a concise summary of a DataFrame. This method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage. Parameters
... | pandas.reference.api.pandas.dataframe.info |
pandas.DataFrame.insert DataFrame.insert(loc, column, value, allow_duplicates=False)[source]
Insert column into DataFrame at specified location. Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True. Parameters
loc:int
Insertion index. Must verify 0 <= loc... | pandas.reference.api.pandas.dataframe.insert |
pandas.DataFrame.interpolate DataFrame.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction=None, limit_area=None, downcast=None, **kwargs)[source]
Fill NaN values using an interpolation method. Please note that only method='linear' is supported for DataFrame/Series with a MultiIndex. Pa... | pandas.reference.api.pandas.dataframe.interpolate |
pandas.DataFrame.isin DataFrame.isin(values)[source]
Whether each element in the DataFrame is contained in values. Parameters
values:iterable, Series, DataFrame or dict
The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys mu... | pandas.reference.api.pandas.dataframe.isin |
pandas.DataFrame.isna DataFrame.isna()[source]
Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered... | pandas.reference.api.pandas.dataframe.isna |
pandas.DataFrame.isnull DataFrame.isnull()[source]
DataFrame.isnull is an alias for DataFrame.isna. Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters suc... | pandas.reference.api.pandas.dataframe.isnull |
pandas.DataFrame.items DataFrame.items()[source]
Iterate over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Yields
label:object
The column names for the DataFrame being iterated over.
content:Series
The column entries ... | pandas.reference.api.pandas.dataframe.items |
pandas.DataFrame.iteritems DataFrame.iteritems()[source]
Iterate over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Yields
label:object
The column names for the DataFrame being iterated over.
content:Series
The column ... | pandas.reference.api.pandas.dataframe.iteritems |
pandas.DataFrame.iterrows DataFrame.iterrows()[source]
Iterate over DataFrame rows as (index, Series) pairs. Yields
index:label or tuple of label
The index of the row. A tuple for a MultiIndex.
data:Series
The data of the row as a Series. See also DataFrame.itertuples
Iterate over DataFrame rows as... | pandas.reference.api.pandas.dataframe.iterrows |
pandas.DataFrame.itertuples DataFrame.itertuples(index=True, name='Pandas')[source]
Iterate over DataFrame rows as namedtuples. Parameters
index:bool, default True
If True, return the index as the first element of the tuple.
name:str or None, default “Pandas”
The name of the returned namedtuples or None t... | pandas.reference.api.pandas.dataframe.itertuples |
pandas.DataFrame.join DataFrame.join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)[source]
Join columns of another DataFrame. Join columns with other DataFrame either on index or on a key column. Efficiently join multiple DataFrame objects by index at once by passing a list. Parameters
other:D... | pandas.reference.api.pandas.dataframe.join |
pandas.DataFrame.keys DataFrame.keys()[source]
Get the ‘info axis’ (see Indexing for more). This is index for Series, columns for DataFrame. Returns
Index
Info axis. | pandas.reference.api.pandas.dataframe.keys |
pandas.DataFrame.kurt DataFrame.kurt(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return unbiased kurtosis over requested axis. Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. Parameters
axis:{index (0), columns ... | pandas.reference.api.pandas.dataframe.kurt |
pandas.DataFrame.kurtosis DataFrame.kurtosis(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return unbiased kurtosis over requested axis. Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1. Parameters
axis:{index (0), ... | pandas.reference.api.pandas.dataframe.kurtosis |
pandas.DataFrame.last DataFrame.last(offset)[source]
Select final periods of time series data based on a date offset. For a DataFrame with a sorted DatetimeIndex, this function selects the last few rows based on a date offset. Parameters
offset:str, DateOffset, dateutil.relativedelta
The offset length of the ... | pandas.reference.api.pandas.dataframe.last |
pandas.DataFrame.last_valid_index DataFrame.last_valid_index()[source]
Return index for last non-NA value or None, if no NA value is found. Returns
scalar:type of index
Notes If all elements are non-NA/null, returns None. Also returns None for empty Series/DataFrame. | pandas.reference.api.pandas.dataframe.last_valid_index |
pandas.DataFrame.le DataFrame.le(other, axis='columns', level=None)[source]
Get Less than or equal to of dataframe and other, element-wise (binary operator le). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) ... | pandas.reference.api.pandas.dataframe.le |
pandas.DataFrame.loc propertyDataFrame.loc
Access a group of rows and columns by label(s) or a boolean array. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are: A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer p... | pandas.reference.api.pandas.dataframe.loc |
pandas.DataFrame.lookup DataFrame.lookup(row_labels, col_labels)[source]
Label-based “fancy indexing” function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair. Deprecated since version 1.2.0: DataFrame.lookup is deprecated, use ... | pandas.reference.api.pandas.dataframe.lookup |
pandas.DataFrame.lt DataFrame.lt(other, axis='columns', level=None)[source]
Get Less than of dataframe and other, element-wise (binary operator lt). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level fo... | pandas.reference.api.pandas.dataframe.lt |
pandas.DataFrame.mad DataFrame.mad(axis=None, skipna=True, level=None)[source]
Return the mean absolute deviation of the values over the requested axis. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the resu... | pandas.reference.api.pandas.dataframe.mad |
pandas.DataFrame.mask DataFrame.mask(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=NoDefault.no_default)[source]
Replace values where the condition is True. Parameters
cond:bool Series/DataFrame, array-like, or callable
Where cond is False, keep the original value. Where True... | pandas.reference.api.pandas.dataframe.mask |
pandas.DataFrame.max DataFrame.max(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return the maximum of the values over the requested axis. If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax. Parameters
axis:{inde... | pandas.reference.api.pandas.dataframe.max |
pandas.DataFrame.mean DataFrame.mean(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return the mean of the values over the requested axis. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null va... | pandas.reference.api.pandas.dataframe.mean |
pandas.DataFrame.median DataFrame.median(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return the median of the values over the requested axis. Parameters
axis:{index (0), columns (1)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/n... | pandas.reference.api.pandas.dataframe.median |
pandas.DataFrame.melt DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)[source]
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set. This function is useful to massage a DataFrame into a format where one or more columns a... | pandas.reference.api.pandas.dataframe.melt |
pandas.DataFrame.memory_usage DataFrame.memory_usage(index=True, deep=False)[source]
Return the memory usage of each column in bytes. The memory usage can optionally include the contribution of the index and elements of object dtype. This value is displayed in DataFrame.info by default. This can be suppressed by se... | pandas.reference.api.pandas.dataframe.memory_usage |
pandas.DataFrame.merge DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)[source]
Merge DataFrame or named Series objects with a database-style join. A named Series object is tre... | pandas.reference.api.pandas.dataframe.merge |
pandas.DataFrame.min DataFrame.min(axis=NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)[source]
Return the minimum of the values over the requested axis. If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin. Parameters
axis:{inde... | pandas.reference.api.pandas.dataframe.min |
pandas.DataFrame.mod DataFrame.mod(other, axis='columns', level=None, fill_value=None)[source]
Get Modulo of dataframe and other, element-wise (binary operator mod). Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmod. Among ... | pandas.reference.api.pandas.dataframe.mod |
pandas.DataFrame.mode DataFrame.mode(axis=0, numeric_only=False, dropna=True)[source]
Get the mode(s) of each element along the selected axis. The mode of a set of values is the value that appears most often. It can be multiple values. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to iter... | pandas.reference.api.pandas.dataframe.mode |
pandas.DataFrame.mul DataFrame.mul(other, axis='columns', level=None, fill_value=None)[source]
Get Multiplication of dataframe and other, element-wise (binary operator mul). Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rmul... | pandas.reference.api.pandas.dataframe.mul |
pandas.DataFrame.multiply DataFrame.multiply(other, axis='columns', level=None, fill_value=None)[source]
Get Multiplication of dataframe and other, element-wise (binary operator mul). Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse ver... | pandas.reference.api.pandas.dataframe.multiply |
pandas.DataFrame.ndim propertyDataFrame.ndim
Return an int representing the number of axes / array dimensions. Return 1 if Series. Otherwise return 2 if DataFrame. See also ndarray.ndim
Number of array dimensions. Examples
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = pd.DataFrame({'co... | pandas.reference.api.pandas.dataframe.ndim |
pandas.DataFrame.ne DataFrame.ne(other, axis='columns', level=None)[source]
Get Not equal to of dataframe and other, element-wise (binary operator ne). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level... | pandas.reference.api.pandas.dataframe.ne |
pandas.DataFrame.nlargest DataFrame.nlargest(n, columns, keep='first')[source]
Return the first n rows ordered by columns in descending order. Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering. This met... | pandas.reference.api.pandas.dataframe.nlargest |
pandas.DataFrame.notna DataFrame.notna()[source]
Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_... | pandas.reference.api.pandas.dataframe.notna |
pandas.DataFrame.notnull DataFrame.notnull()[source]
DataFrame.notnull is an alias for DataFrame.notna. Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not consid... | pandas.reference.api.pandas.dataframe.notnull |
pandas.DataFrame.nsmallest DataFrame.nsmallest(n, columns, keep='first')[source]
Return the first n rows ordered by columns in ascending order. Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering. This me... | pandas.reference.api.pandas.dataframe.nsmallest |
pandas.DataFrame.nunique DataFrame.nunique(axis=0, dropna=True)[source]
Count number of distinct elements in specified axis. Return Series with number of distinct elements. Can ignore NaN values. Parameters
axis:{0 or ‘index’, 1 or ‘columns’}, default 0
The axis to use. 0 or ‘index’ for row-wise, 1 or ‘column... | pandas.reference.api.pandas.dataframe.nunique |
pandas.DataFrame.pad DataFrame.pad(axis=None, inplace=False, limit=None, downcast=None)[source]
Synonym for DataFrame.fillna() with method='ffill'. Returns
Series/DataFrame or None
Object with missing values filled or None if inplace=True. | pandas.reference.api.pandas.dataframe.pad |
pandas.DataFrame.pct_change DataFrame.pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)[source]
Percentage change between the current and a prior element. Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a ti... | pandas.reference.api.pandas.dataframe.pct_change |
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