doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
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pandas.Series.dt.day_of_week Series.dt.day_of_week
The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the dt accessor) or... | pandas.reference.api.pandas.series.dt.day_of_week |
pandas.Series.dt.day_of_year Series.dt.day_of_year
The ordinal day of the year. | pandas.reference.api.pandas.series.dt.day_of_year |
pandas.Series.dt.dayofweek Series.dt.dayofweek
The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the dt accessor) or Dat... | pandas.reference.api.pandas.series.dt.dayofweek |
pandas.Series.dt.dayofyear Series.dt.dayofyear
The ordinal day of the year. | pandas.reference.api.pandas.series.dt.dayofyear |
pandas.Series.dt.days Series.dt.days
Number of days for each element. | pandas.reference.api.pandas.series.dt.days |
pandas.Series.dt.days_in_month Series.dt.days_in_month
The number of days in the month. | pandas.reference.api.pandas.series.dt.days_in_month |
pandas.Series.dt.daysinmonth Series.dt.daysinmonth
The number of days in the month. | pandas.reference.api.pandas.series.dt.daysinmonth |
pandas.Series.dt.end_time Series.dt.end_time | pandas.reference.api.pandas.series.dt.end_time |
pandas.Series.dt.floor Series.dt.floor(*args, **kwargs)[source]
Perform floor operation on the data to the specified freq. Parameters
freq:str or Offset
The frequency level to floor the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible fre... | pandas.reference.api.pandas.series.dt.floor |
pandas.Series.dt.freq Series.dt.freq | pandas.reference.api.pandas.series.dt.freq |
pandas.Series.dt.hour Series.dt.hour
The hours of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="h")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 01:00:00
2 2000-01-01 02:00:00
dtype: datetime64[ns]
>>> datetime_series.dt.hour
0 ... | pandas.reference.api.pandas.series.dt.hour |
pandas.Series.dt.is_leap_year Series.dt.is_leap_year
Boolean indicator if the date belongs to a leap year. A leap year is a year, which has 366 days (instead of 365) including 29th of February as an intercalary day. Leap years are years which are multiples of four with the exception of years divisible by 100 but no... | pandas.reference.api.pandas.series.dt.is_leap_year |
pandas.Series.dt.is_month_end Series.dt.is_month_end
Indicates whether the date is the last day of the month. Returns
Series or array
For Series, returns a Series with boolean values. For DatetimeIndex, returns a boolean array. See also is_month_start
Return a boolean indicating whether the date is the f... | pandas.reference.api.pandas.series.dt.is_month_end |
pandas.Series.dt.is_month_start Series.dt.is_month_start
Indicates whether the date is the first day of the month. Returns
Series or array
For Series, returns a Series with boolean values. For DatetimeIndex, returns a boolean array. See also is_month_start
Return a boolean indicating whether the date is ... | pandas.reference.api.pandas.series.dt.is_month_start |
pandas.Series.dt.is_quarter_end Series.dt.is_quarter_end
Indicator for whether the date is the last day of a quarter. Returns
is_quarter_end:Series or DatetimeIndex
The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See... | pandas.reference.api.pandas.series.dt.is_quarter_end |
pandas.Series.dt.is_quarter_start Series.dt.is_quarter_start
Indicator for whether the date is the first day of a quarter. Returns
is_quarter_start:Series or DatetimeIndex
The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. ... | pandas.reference.api.pandas.series.dt.is_quarter_start |
pandas.Series.dt.is_year_end Series.dt.is_year_end
Indicate whether the date is the last day of the year. Returns
Series or DatetimeIndex
The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See also is_year_start
Similar... | pandas.reference.api.pandas.series.dt.is_year_end |
pandas.Series.dt.is_year_start Series.dt.is_year_start
Indicate whether the date is the first day of a year. Returns
Series or DatetimeIndex
The same type as the original data with boolean values. Series will have the same name and index. DatetimeIndex will have the same name. See also is_year_end
Simila... | pandas.reference.api.pandas.series.dt.is_year_start |
pandas.Series.dt.microsecond Series.dt.microsecond
The microseconds of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="us")
... )
>>> datetime_series
0 2000-01-01 00:00:00.000000
1 2000-01-01 00:00:00.000001
2 2000-01-01 00:00:00.000002
dtype: date... | pandas.reference.api.pandas.series.dt.microsecond |
pandas.Series.dt.microseconds Series.dt.microseconds
Number of microseconds (>= 0 and less than 1 second) for each element. | pandas.reference.api.pandas.series.dt.microseconds |
pandas.Series.dt.minute Series.dt.minute
The minutes of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="T")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 00:01:00
2 2000-01-01 00:02:00
dtype: datetime64[ns]
>>> datetime_series.dt.min... | pandas.reference.api.pandas.series.dt.minute |
pandas.Series.dt.month Series.dt.month
The month as January=1, December=12. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="M")
... )
>>> datetime_series
0 2000-01-31
1 2000-02-29
2 2000-03-31
dtype: datetime64[ns]
>>> datetime_series.dt.month
0 1
1 2
2 ... | pandas.reference.api.pandas.series.dt.month |
pandas.Series.dt.month_name Series.dt.month_name(*args, **kwargs)[source]
Return the month names of the DateTimeIndex with specified locale. Parameters
locale:str, optional
Locale determining the language in which to return the month name. Default is English locale. Returns
Index
Index of month names. ... | pandas.reference.api.pandas.series.dt.month_name |
pandas.Series.dt.nanosecond Series.dt.nanosecond
The nanoseconds of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="ns")
... )
>>> datetime_series
0 2000-01-01 00:00:00.000000000
1 2000-01-01 00:00:00.000000001
2 2000-01-01 00:00:00.000000002
dtype... | pandas.reference.api.pandas.series.dt.nanosecond |
pandas.Series.dt.nanoseconds Series.dt.nanoseconds
Number of nanoseconds (>= 0 and less than 1 microsecond) for each element. | pandas.reference.api.pandas.series.dt.nanoseconds |
pandas.Series.dt.normalize Series.dt.normalize(*args, **kwargs)[source]
Convert times to midnight. The time component of the date-time is converted to midnight i.e. 00:00:00. This is useful in cases, when the time does not matter. Length is unaltered. The timezones are unaffected. This method is available on Series... | pandas.reference.api.pandas.series.dt.normalize |
pandas.Series.dt.quarter Series.dt.quarter
The quarter of the date. | pandas.reference.api.pandas.series.dt.quarter |
pandas.Series.dt.qyear Series.dt.qyear | pandas.reference.api.pandas.series.dt.qyear |
pandas.Series.dt.round Series.dt.round(*args, **kwargs)[source]
Perform round operation on the data to the specified freq. Parameters
freq:str or Offset
The frequency level to round the index to. Must be a fixed frequency like ‘S’ (second) not ‘ME’ (month end). See frequency aliases for a list of possible fre... | pandas.reference.api.pandas.series.dt.round |
pandas.Series.dt.second Series.dt.second
The seconds of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="s")
... )
>>> datetime_series
0 2000-01-01 00:00:00
1 2000-01-01 00:00:01
2 2000-01-01 00:00:02
dtype: datetime64[ns]
>>> datetime_series.dt.sec... | pandas.reference.api.pandas.series.dt.second |
pandas.Series.dt.seconds Series.dt.seconds
Number of seconds (>= 0 and less than 1 day) for each element. | pandas.reference.api.pandas.series.dt.seconds |
pandas.Series.dt.start_time Series.dt.start_time | pandas.reference.api.pandas.series.dt.start_time |
pandas.Series.dt.strftime Series.dt.strftime(*args, **kwargs)[source]
Convert to Index using specified date_format. Return an Index of formatted strings specified by date_format, which supports the same string format as the python standard library. Details of the string format can be found in python string format d... | pandas.reference.api.pandas.series.dt.strftime |
pandas.Series.dt.time Series.dt.time
Returns numpy array of datetime.time objects. The time part of the Timestamps. | pandas.reference.api.pandas.series.dt.time |
pandas.Series.dt.timetz Series.dt.timetz
Returns numpy array of datetime.time objects with timezone information. The time part of the Timestamps. | pandas.reference.api.pandas.series.dt.timetz |
pandas.Series.dt.to_period Series.dt.to_period(*args, **kwargs)[source]
Cast to PeriodArray/Index at a particular frequency. Converts DatetimeArray/Index to PeriodArray/Index. Parameters
freq:str or Offset, optional
One of pandas’ offset strings or an Offset object. Will be inferred by default. Returns
P... | pandas.reference.api.pandas.series.dt.to_period |
pandas.Series.dt.to_pydatetime Series.dt.to_pydatetime()[source]
Return the data as an array of datetime.datetime objects. Timezone information is retained if present. Warning Python’s datetime uses microsecond resolution, which is lower than pandas (nanosecond). The values are truncated. Returns
numpy.ndarray... | pandas.reference.api.pandas.series.dt.to_pydatetime |
pandas.Series.dt.to_pytimedelta Series.dt.to_pytimedelta()[source]
Return an array of native datetime.timedelta objects. Python’s standard datetime library uses a different representation timedelta’s. This method converts a Series of pandas Timedeltas to datetime.timedelta format with the same length as the origina... | pandas.reference.api.pandas.series.dt.to_pytimedelta |
pandas.Series.dt.total_seconds Series.dt.total_seconds(*args, **kwargs)[source]
Return total duration of each element expressed in seconds. This method is available directly on TimedeltaArray, TimedeltaIndex and on Series containing timedelta values under the .dt namespace. Returns
seconds:[ndarray, Float64Inde... | pandas.reference.api.pandas.series.dt.total_seconds |
pandas.Series.dt.tz Series.dt.tz
Return the timezone. Returns
datetime.tzinfo, pytz.tzinfo.BaseTZInfo, dateutil.tz.tz.tzfile, or None
Returns None when the array is tz-naive. | pandas.reference.api.pandas.series.dt.tz |
pandas.Series.dt.tz_convert Series.dt.tz_convert(*args, **kwargs)[source]
Convert tz-aware Datetime Array/Index from one time zone to another. Parameters
tz:str, pytz.timezone, dateutil.tz.tzfile or None
Time zone for time. Corresponding timestamps would be converted to this time zone of the Datetime Array/In... | pandas.reference.api.pandas.series.dt.tz_convert |
pandas.Series.dt.tz_localize Series.dt.tz_localize(*args, **kwargs)[source]
Localize tz-naive Datetime Array/Index to tz-aware Datetime Array/Index. This method takes a time zone (tz) naive Datetime Array/Index object and makes this time zone aware. It does not move the time to another time zone. This method can al... | pandas.reference.api.pandas.series.dt.tz_localize |
pandas.Series.dt.week Series.dt.week
The week ordinal of the year. Deprecated since version 1.1.0. Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead. | pandas.reference.api.pandas.series.dt.week |
pandas.Series.dt.weekday Series.dt.weekday
The day of the week with Monday=0, Sunday=6. Return the day of the week. It is assumed the week starts on Monday, which is denoted by 0 and ends on Sunday which is denoted by 6. This method is available on both Series with datetime values (using the dt accessor) or Datetim... | pandas.reference.api.pandas.series.dt.weekday |
pandas.Series.dt.weekofyear Series.dt.weekofyear
The week ordinal of the year. Deprecated since version 1.1.0. Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead. | pandas.reference.api.pandas.series.dt.weekofyear |
pandas.Series.dt.year Series.dt.year
The year of the datetime. Examples
>>> datetime_series = pd.Series(
... pd.date_range("2000-01-01", periods=3, freq="Y")
... )
>>> datetime_series
0 2000-12-31
1 2001-12-31
2 2002-12-31
dtype: datetime64[ns]
>>> datetime_series.dt.year
0 2000
1 2001
2 2002
dt... | pandas.reference.api.pandas.series.dt.year |
pandas.Series.dtype propertySeries.dtype
Return the dtype object of the underlying data. | pandas.reference.api.pandas.series.dtype |
pandas.Series.dtypes propertySeries.dtypes
Return the dtype object of the underlying data. | pandas.reference.api.pandas.series.dtypes |
pandas.Series.duplicated Series.duplicated(keep='first')[source]
Indicate duplicate Series values. Duplicated values are indicated as True values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated. Parameters
keep:{‘first’, ‘last... | pandas.reference.api.pandas.series.duplicated |
pandas.Series.empty propertySeries.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 without n... | pandas.reference.api.pandas.series.empty |
pandas.Series.eq Series.eq(other, level=None, fill_value=None, axis=0)[source]
Return Equal to of series and other, element-wise (binary operator eq). Equivalent to series == other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar value
... | pandas.reference.api.pandas.series.eq |
pandas.Series.equals Series.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 need to h... | pandas.reference.api.pandas.series.equals |
pandas.Series.ewm Series.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:float, op... | pandas.reference.api.pandas.series.ewm |
pandas.Series.expanding Series.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
If F... | pandas.reference.api.pandas.series.expanding |
pandas.Series.explode Series.explode(ignore_index=False)[source]
Transform each element of a list-like to a row. New in version 0.25.0. Parameters
ignore_index:bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1. New in version 1.1.0. Returns
Series
Exploded lists to rows... | pandas.reference.api.pandas.series.explode |
pandas.Series.factorize Series.factorize(sort=False, na_sentinel=- 1)[source]
Encode the object as an enumerated type or categorical variable. This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values. factorize is available as both a top-level fun... | pandas.reference.api.pandas.series.factorize |
pandas.Series.ffill Series.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.series.ffill |
pandas.Series.fillna Series.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 specify... | pandas.reference.api.pandas.series.fillna |
pandas.Series.filter Series.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-like
... | pandas.reference.api.pandas.series.filter |
pandas.Series.first Series.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 length of ... | pandas.reference.api.pandas.series.first |
pandas.Series.first_valid_index Series.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.series.first_valid_index |
pandas.Series.flags propertySeries.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”. Flags ref... | pandas.reference.api.pandas.series.flags |
pandas.Series.floordiv Series.floordiv(other, level=None, fill_value=None, axis=0)[source]
Return Integer division of series and other, element-wise (binary operator floordiv). Equivalent to series // other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
oth... | pandas.reference.api.pandas.series.floordiv |
pandas.Series.ge Series.ge(other, level=None, fill_value=None, axis=0)[source]
Return Greater than or equal to of series and other, element-wise (binary operator ge). Equivalent to series >= other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series ... | pandas.reference.api.pandas.series.ge |
pandas.Series.get Series.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, "high"],... | pandas.reference.api.pandas.series.get |
pandas.Series.groupby Series.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 Series using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a fun... | pandas.reference.api.pandas.series.groupby |
pandas.Series.gt Series.gt(other, level=None, fill_value=None, axis=0)[source]
Return Greater than of series and other, element-wise (binary operator gt). Equivalent to series > other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar val... | pandas.reference.api.pandas.series.gt |
pandas.Series.hasnans propertySeries.hasnans
Return True if there are any NaNs. Enables various performance speedups. | pandas.reference.api.pandas.series.hasnans |
pandas.Series.head Series.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, equivalent ... | pandas.reference.api.pandas.series.head |
pandas.Series.hist Series.hist(by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, backend=None, legend=False, **kwargs)[source]
Draw histogram of the input series using matplotlib. Parameters
by:object, optional
If passed, then used to form histograms f... | pandas.reference.api.pandas.series.hist |
pandas.Series.iat propertySeries.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. See al... | pandas.reference.api.pandas.series.iat |
pandas.Series.idxmax Series.idxmax(axis=0, skipna=True, *args, **kwargs)[source]
Return the row label of the maximum value. If multiple values equal the maximum, the first row label with that value is returned. Parameters
axis:int, default 0
For compatibility with DataFrame.idxmax. Redundant for application o... | pandas.reference.api.pandas.series.idxmax |
pandas.Series.idxmin Series.idxmin(axis=0, skipna=True, *args, **kwargs)[source]
Return the row label of the minimum value. If multiple values equal the minimum, the first row label with that value is returned. Parameters
axis:int, default 0
For compatibility with DataFrame.idxmin. Redundant for application o... | pandas.reference.api.pandas.series.idxmin |
pandas.Series.iloc propertySeries.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]. A sl... | pandas.reference.api.pandas.series.iloc |
pandas.Series.index Series.index
The index (axis labels) of the Series. | pandas.reference.api.pandas.series.index |
pandas.Series.infer_objects Series.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
conver... | pandas.reference.api.pandas.series.infer_objects |
pandas.Series.info Series.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=True)[source]
Print a concise summary of a Series. This method prints information about a Series including the index dtype, non-null values and memory usage. New in version 1.4.0. Parameters
data:Series
Seri... | pandas.reference.api.pandas.series.info |
pandas.Series.interpolate Series.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. Paramete... | pandas.reference.api.pandas.series.interpolate |
pandas.Series.is_monotonic propertySeries.is_monotonic
Return boolean if values in the object are monotonic_increasing. Returns
bool | pandas.reference.api.pandas.series.is_monotonic |
pandas.Series.is_monotonic_decreasing propertySeries.is_monotonic_decreasing
Return boolean if values in the object are monotonic_decreasing. Returns
bool | pandas.reference.api.pandas.series.is_monotonic_decreasing |
pandas.Series.is_monotonic_increasing propertySeries.is_monotonic_increasing
Alias for is_monotonic. | pandas.reference.api.pandas.series.is_monotonic_increasing |
pandas.Series.is_unique propertySeries.is_unique
Return boolean if values in the object are unique. Returns
bool | pandas.reference.api.pandas.series.is_unique |
pandas.Series.isin Series.isin(values)[source]
Whether elements in Series are contained in values. Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of values exactly. Parameters
values:set or list-like
The sequence of values to test. Passing in a sin... | pandas.reference.api.pandas.series.isin |
pandas.Series.isna Series.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 NA va... | pandas.reference.api.pandas.series.isna |
pandas.Series.isnull Series.isnull()[source]
Series.isnull is an alias for Series.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 such as empty s... | pandas.reference.api.pandas.series.isnull |
pandas.Series.item Series.item()[source]
Return the first element of the underlying data as a Python scalar. Returns
scalar
The first element of %(klass)s. Raises
ValueError
If the data is not length-1. | pandas.reference.api.pandas.series.item |
pandas.Series.items Series.items()[source]
Lazily iterate over (index, value) tuples. This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator. Returns
iterable
Iterable of tuples containing the (index, value) pairs from a Series. See also DataFrame.it... | pandas.reference.api.pandas.series.items |
pandas.Series.iteritems Series.iteritems()[source]
Lazily iterate over (index, value) tuples. This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator. Returns
iterable
Iterable of tuples containing the (index, value) pairs from a Series. See also Data... | pandas.reference.api.pandas.series.iteritems |
pandas.Series.keys Series.keys()[source]
Return alias for index. Returns
Index
Index of the Series. | pandas.reference.api.pandas.series.keys |
pandas.Series.kurt Series.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)}
Axis for the... | pandas.reference.api.pandas.series.kurt |
pandas.Series.kurtosis Series.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)}
Axis... | pandas.reference.api.pandas.series.kurtosis |
pandas.Series.last Series.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 data t... | pandas.reference.api.pandas.series.last |
pandas.Series.last_valid_index Series.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.series.last_valid_index |
pandas.Series.le Series.le(other, level=None, fill_value=None, axis=0)[source]
Return Less than or equal to of series and other, element-wise (binary operator le). Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or ... | pandas.reference.api.pandas.series.le |
pandas.Series.loc propertySeries.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 positio... | pandas.reference.api.pandas.series.loc |
pandas.Series.lt Series.lt(other, level=None, fill_value=None, axis=0)[source]
Return Less than of series and other, element-wise (binary operator lt). Equivalent to series < other, but with support to substitute a fill_value for missing data in either one of the inputs. Parameters
other:Series or scalar value
... | pandas.reference.api.pandas.series.lt |
pandas.Series.mad Series.mad(axis=None, skipna=True, level=None)[source]
Return the mean absolute deviation of the values over the requested axis. Parameters
axis:{index (0)}
Axis for the function to be applied on.
skipna:bool, default True
Exclude NA/null values when computing the result.
level:int or ... | pandas.reference.api.pandas.series.mad |
pandas.Series.map Series.map(arg, na_action=None)[source]
Map values of Series according to an input mapping or function. Used for substituting each value in a Series with another value, that may be derived from a function, a dict or a Series. Parameters
arg:function, collections.abc.Mapping subclass or Series
... | pandas.reference.api.pandas.series.map |
pandas.Series.mask Series.mask(cond, other=nan, inplace=False, axis=None, level=None, errors=NoDefault.no_default, 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. Whe... | pandas.reference.api.pandas.series.mask |
pandas.Series.max Series.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:{index (0)}... | pandas.reference.api.pandas.series.max |
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