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pandas_series/pandas-settingwithcopywarning0_68_0.txt |
>>> copy_of_arr = arr.copy()
>>> copy_of_arr
array([ 1, 2, 4, 8, 16, 32])
>>> copy_of_arr.base is None
True
>>> copy_of_arr.flags.owndata
True
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pandas_series/pandas_series_299_6.txt | ob/v2.2.1/pandas/core/series.py#L263-L6622) #
One-dimensional ndarray with axis labels (including time series).
Labels need not be unique but must be a hashable type. The object supports
both integer- and label-based indexing and provides a host of methods for
performing operations involving the index. Statisti... | |
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pandas_series/pandas_series_259_6.txt | d.end_time ` ](pandas.Period.end_time.html#pandas.Period.end_time
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Return the end Timestamp.
[ ` Period.dayofyear ` ](pandas.Period.dayofyear.html#pandas.Period.dayofyear
"pandas.Period.dayofyear")
Return the day of year.
[ ` Period.daysinmonth `
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pandas_series/pandas_series_106_6.txt | ther any element is True, potentially over an axis.
Returns False unless there is at least one element within a series or along a
Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).
Parameters :
**axis** {0 or âindexâ, 1 or âcolumnsâ, None}, default 0
Indicate which axis or ... | |
pandas_series/pandas_series_69_6.txt | s with MultiIndex to produce DataFrame.
Parameters :
**level** int, str, or list of these, default last level
Level(s) to unstack, can pass level name.
**fill_value** scalar value, default None
Value to use when replacing NaN values.
**sort** bool, default True
Sort the level(s) in the r... | |
pandas_series/pandas_series_137_6.txt | ters :
**freq** str or Offset
The frequency level to floor the index to. Must be a fixed frequency like
âSâ (second) not âMEâ (month end). See [ frequency aliases
](../../user_guide/timeseries.html#timeseries-offset-aliases) for a list of
possible freq values.
**ambiguous** âinferâ, bool-n... | |
pandas_series/pandas_series_251_6.txt | d.Series(['Ant', 'Bear', 'Cow'])
>>> s
0 Ant
1 Bear
2 Cow
dtype: object
>>> s.ndim
1
For Index:
>>> idx = pd.Index([1, 2, 3])
>>> idx
Index([1, 2, 3], dtype='int64')
>>> idx.ndim
1
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pandas_series/pandas_series_188_6.txt | on-null values in the Series.
See also
[ ` DataFrame.count ` ](pandas.DataFrame.count.html#pandas.DataFrame.count
"pandas.DataFrame.count")
Count non-NA cells for each column or row.
Examples
>>> s = pd.Series([0.0, 1.0, np.nan])
>>> s.count()
2
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pandas_series/pandas_series_202_8.txt | k i 12 2
mark ii 0 4
Single index tuple. Note this returns a Series.
>>> df.loc[('cobra', 'mark ii')]
max_speed 0
shield 4
Name: (cobra, mark ii), dtype: int64
Single label for row and column. Similar to passing in a tuple, this returns a... | |
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pandas_series/pandas_series_242_7.txt | âcolumnâ do not
support index=False .
**indent** int, optional
Length of whitespace used to indent each record.
**storage_options** dict, optional
Extra options that make sense for a particular storage connection, e.g. host,
port, username, password, etc. For HTTP(S) URLs the key-value pairs are
... | |
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pandas_series/pandas_series_63_6.txt | gorical(["a", "b", "c", "a"], categories=["a", "b"])
>>> ser = pd.Series(raw_cate)
>>> ser.cat.codes
0 0
1 1
2 -1
3 0
dtype: int8
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pandas_series/selecting-in-pandas-using-where-and-mask2_17_0.txt | * what an index is and why it is needed
* how to select data in both a Series and DataFrame .
* the difference between .loc, .iloc, .ix, and [] and when (and if) you should use them.
* slicing, and how pandas slicing compares to regular Python slicing
* boolean indexing
* selecting via callable... | |
pandas_series/pandas-settingwithcopywarning0_124_0.txt | Remove ads
### Indexing in pandas: Copies and Views | |
pandas_series/dataframe-indexing.html6_0_0.txt | * * Dask
* Distributed
* Dask ML
* Examples
* Ecosystem
* Community
Toggle navigation sidebar | |
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pandas_series/pandas-settingwithcopywarning0_207_0.txt | ## Keep reading Real Python by creating a free account or signing in:
Continue » | |
pandas_series/pandas_series_166_6.txt | () `
](https://docs.python.org/3/library/stdtypes.html#str.casefold "\(in Python
v3.12\)") .
Returns :
Series or Index of object
See also
[ ` Series.str.lower ` ](pandas.Series.str.lower.html#pandas.Series.str.lower
"pandas.Series.str.lower")
Converts all characters to lowercase.
[ ` Series.s... | |
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pandas_series/pandas_series_147_6.txt | the dot product between the Series and another one, or
the Series and each columns of a DataFrame, or the Series and each columns of
an array.
It can also be called using self @ other .
Parameters :
**other** Series, DataFrame or array-like
The other object to compute the dot product with its columns... | |
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pandas_series/pandas-settingwithcopywarning0_157_0.txt |
>>> df["b":"d"]["z"] = 0
__main__:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexin... | |
pandas_series/pandas.DataFrame.mask.html4_13_0.txt | axis int, default None
| |
pandas_series/pandas_series_197_6.txt | 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: datetime64[ns]
>>> datetime_series.dt.microsecond
0 0
1 1
2 2
dtype: int32
... | |
pandas_series/pandas_series_167_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_118_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas-settingwithcopywarning0_163_0.txt | Python
>>> df = pd.DataFrame(
... data={("powers", "x"): 2**np.arange(5),
... ("powers", "y"): 3**np.arange(5),
... ("random", "z"): np.array([45, 98, 24, 11, 64])},
... index=["a", "b", "c", "d", "e"]
... )
>>> df
powers random
... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_35_0.txt | Thanks for subscribing! Check your email for details on getting the cheatsheet
and Jupyter notebooks.
Copyright © 2024 wrighters.io | |
pandas_series/pandas_series_209_3.txt | .sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.... | |
pandas_series/pandas_series_121_6.txt | enate strings in the Series/Index with given separator.
If others is specified, this function concatenates the Series/Index and
elements of others element-wise. If others is not passed, then all values
in the Series/Index are concatenated into a single string with a given sep .
Parameters :
**others**... | |
pandas_series/pandas_series_90_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_164_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categor... | |
pandas_series/pandas-settingwithcopywarning0_141_0.txt |
>>> df["z"][mask] = 0
>>> df
x y z
a 1 1 0
b 2 3 98
c 4 9 0
d 8 27 0
e 16 81 64
Copied! | |
pandas_series/pandas_series_124_4.txt | ndas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
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* [ pandas.Series... | |
pandas_series/pandas_series_50_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_299_9.txt | ith other Series, excluding missing values.
[ ` count ` ](pandas.Series.count.html#pandas.Series.count "pandas.Series.count") () | Return number of non-NA/null observations in the Series.
[ ` cov ` ](pandas.Series.cov.html#pandas.Series.cov "pandas.Series.cov") (other[, min_periods, ddof]) | Compute cov... | |
pandas_series/pandas_series_243_4.txt | pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Seri... | |
pandas_series/pandas_series_327_6.txt | ` 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** {âfirstâ, âlastâ, False}, default âfirstâ
Method to handle dropping duplicates:
* âfirstâ : Mark duplicates as ` True... | |
pandas_series/pandas_series_12_3.txt | sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.time ](pandas.Series.dt.time.html)
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* [ pandas.Series.dt.year ](pandas.Series.d... | |
pandas_series/pandas_series_169_2.txt | eries.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
* [ pandas.Series.idxm... | |
pandas_series/pandas_series_161_6.txt | :
bool
Examples
>>> s = pd.Series([3, 2, 2, 1])
>>> s.is_monotonic_decreasing
True
>>> s = pd.Series([1, 2, 3])
>>> s.is_monotonic_decreasing
False
[ __ previous pandas.Series.is_monotonic_increasing
](pandas.Series.is_monotonic_increasing.html "pre... | |
pandas_series/pandas_series_99_5.txt | ies.cat.rename_categories ](pandas.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
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pandas_series/pandas_series_187_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_282_1.txt | ruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Series.combine_first ](pandas.Serie... | |
pandas_series/pandas-settingwithcopywarning0_69_0.txt | As you can see, ` copy_of_arr ` doesn’t have ` .base ` . To be more precise,
the value of ` copy_of_arr.base ` is ` None ` . The attribute `
.flags.owndata ` is ` True ` . This means that ` copy_of_arr ` owns data:
The image above shows that ` arr ` and ` copy_of_arr ` contain different
instances of data values. | |
pandas_series/pandas-settingwithcopywarning0_119_0.txt | Copied!
In this example, you start from the two-dimensional array ` arr ` . You apply
slices for rows. Using the colon syntax ( ` : ` ), which is equivalent to `
slice(None) ` , means that you want to take all rows. | |
pandas_series/pandas_series_284_6.txt | series and other, element-wise (binary operator mul
).
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
**level** int or name
Broadcast across a level, matching Index val... | |
pandas_series/pandas_series_130_4.txt | (pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.contains.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_228_1.txt | ediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
* [ pandas.Series.rpow ](pandas.Series.rpow.html)
* [ pandas.Series.combine ](pandas.Series.combine.html)
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* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
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* [ pandas.Series.idxmax... | |
pandas_series/pandas_series_11_6.txt | n of series and other, element-wise (binary operator
truediv ).
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
**level** int or name
Broadcast across a level, matching I... | |
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pandas_series/pandas_series_259_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_297_3.txt | ml)
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* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series.cat.rem... |
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