id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_180_4.txt | ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_111_5.txt | pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_91_4.txt | das.Series.str.capitalize.html)
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pandas_series/pandas_series_10_2.txt | vel.html)
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pandas_series/pandas-settingwithcopywarning0_185_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], dtype=float)},
... index=["a", "b", "c", "d", "e"]
... )
>>> pd.set_option("mode.cha... | |
pandas_series/pandas_series_116_5.txt | Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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* [ pandas.Series.cat... | |
pandas_series/pandas-settingwithcopywarning0_91_0.txt | NumPy arrays and pandas objects ( ` DataFrame ` and ` Series ` ) implement
special methods that enable referencing, assigning, and deleting values in a
style similar to that of containers :
* ` .__getitem__() ` references values.
* ` .__setitem__() ` assigns values.
* ` .__delitem__() ` deletes values. | |
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pandas_series/pandas_series_53_6.txt | ries and other, element-wise (binary operator sub ).
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 value... | |
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pandas_series/pandas-settingwithcopywarning0_173_0.txt |
>>> df.loc[["a", "b"], ("powers", "x")] = 0
>>> df
powers random
x y z
a 0 1 45
b 0 3 98
c 4 9 24
d 8 27 11
e 16 81 64
Copied! | |
pandas_series/pandas_series_74_2.txt | ries.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_221_7.txt | tyle.Styler.to_excel")
Add styles to Excel sheet.
Notes
For compatibility with [ ` to_csv() `
](pandas.DataFrame.to_csv.html#pandas.DataFrame.to_csv
"pandas.DataFrame.to_csv") , to_excel serializes lists and dicts to strings
before writing.
Once a workbook has been saved it is not possible to write further d... | |
pandas_series/pandas_series_172_6.txt | 0+00:00", "2/1/2020 11:00:00+00:00"])
>>> s = pd.to_datetime(s)
>>> s
0 2020-01-01 10:00:00+00:00
1 2020-02-01 11:00:00+00:00
dtype: datetime64[ns, UTC]
>>> s.dt.dayofyear
0 1
1 32
dtype: int32
For DatetimeIndex:
>>> idx = pd.DatetimeIndex(["1/1/2020 10:... | |
pandas_series/pandas_series_79_2.txt | vel.html)
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pandas_series/pandas-settingwithcopywarning0_189_0.txt | Although you can suppress it, keep in mind that the ` SettingWithCopyWarning `
can be very useful in notifying you about improper code.
## Conclusion | |
pandas_series/pandas.DataFrame.mask.html4_6_0.txt | DataFrame. mask ( cond , other = _NoDefault.no_default , * ,
inplace = False , axis = None , level = None ) [source] #
| |
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pandas_series/pandas_series_322_6.txt | , float)
or a pandas scalar (for Timestamp/Timedelta/Interval/Period)
Returns :
iterator
Examples
>>> s = pd.Series([1, 2, 3])
>>> for x in s:
... print(x)
1
2
3
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pandas_series/pandas-settingwithcopywarning0_41_0.txt | Python
>>> df = pd.DataFrame(data=data, index=index)
>>> df.loc[mask, "z"] = 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
| |
pandas_series/pandas_series_213_4.txt | ndas.Series.str.capitalize.html)
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pandas_series/pandas-settingwithcopywarning0_31_0.txt | Finally, you initialize the DataFrame ` df ` that contains the information
from ` data ` and ` index ` . You can visualize it like this:
Here’s a breakdown of the main information contained in the DataFrame: | |
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pandas_series/pandas_series_299_14.txt | pna, numeric_only, min_count]) | Return the sum of the values over the requested axis.
` swapaxes ` (axis1, axis2[, copy]) | (DEPRECATED) Interchange axes and swap values axes appropriately.
[ ` swaplevel ` ](pandas.Series.swaplevel.html#pandas.Series.swaplevel "pandas.Series.swaplevel") ([i, j, copy]... | |
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pandas_series/pandas_series_316_7.txt | eys contain NA values, NA values together with
row/column will be dropped. If False, NA values will also be treated as the
key in groups.
Returns :
pandas.api.typing.SeriesGroupBy
Returns a groupby object that contains information about the groups.
See also
[ ` resample ` ](pandas.Series.resample.htm... | |
pandas_series/pandas_series_116_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_135_6.txt | ource] ](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L6889-L7037) #
Convert columns to the best possible dtypes using dtypes supporting ` pd.NA `
.
Parameters :
**infer_objects** bool, default True
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pandas_series/pandas_series_249_6.txt | ries objects by filling null values in one Series with non-null
values from the other Series. Result index will be the union of the two
indexes.
Parameters :
**other** Series
The value(s) to be used for filling null values.
Returns :
Series
The result of combining the provided Series wit... | |
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pandas_series/pandas_series_299_7.txt | das.Series.empty.html#pandas.Series.empty "pandas.Series.empty") | Indicator whether Series/DataFrame is empty.
[ ` flags ` ](pandas.Series.flags.html#pandas.Series.flags "pandas.Series.flags") | Get the properties associated with this pandas object.
[ ` hasnans ` ](pandas.Series.hasnans.html#pandas.Series.ha... | |
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pandas_series/pandas_series_326_6.txt | 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
**level** int or name
Broadcast across a level, matching Index values on t... | |
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pandas_series/selecting-in-pandas-using-where-and-mask2_22_0.txt | One thing I noticed about this data set last time was that there were a lot of
` NaN ` values because of the different treatment of salaried and hourly
employees. As a result, there’s a column for annual salary, and separate
columns for typical hours and hourly rates. What if we just want to know what
a typical full sa... | |
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pandas_series/pandas_series_118_6.txt | g the Python string method [ ` str.isnumeric() `
](https://docs.python.org/3/library/stdtypes.html#str.isnumeric "\(in Python
v3.12\)") for each element of the Series/Index. If a string has zero
characters, ` False ` is returned for that check.
Returns :
Series or Index of bool
Series or Index of bool... | |
pandas_series/selecting-in-pandas-using-where-and-mask2_32_0.txt | * Options to run pandas DataFrame.apply in parallel
* Unit testing Python code in Jupyter notebooks
* Parameterizing and automating Jupyter notebooks with papermill
* Profiling Python code with py-spy
* Indexing time series data in pandas
Search for: Search | |
pandas_series/pandas-settingwithcopywarning0_56_0.txt | Now that you have ` arr ` , you can use it to create other arrays. Let’s first
extract the second and fourth elements of ` arr ` ( ` 2 ` and ` 8 ` ) as a new
array. There are several ways to do this:
Python | |
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pandas_series/pandas_series_112_2.txt | vel.html)
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... | |
pandas_series/pandas-settingwithcopywarning0_27_0.txt | Let’s see an example. You’ll start by creating a pandas DataFrame :
Python | |
pandas_series/pandas_series_250_5.txt | andas.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_categories.html)
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pandas_series/pandas-settingwithcopywarning0_146_0.txt |
>>> df = pd.DataFrame(data=data, index=index)
>>> df.loc[["a", "c", "e"]]["z"] = 0 # Assignment fails, no warning
>>> df
x y z
a 1 1 45
b 2 3 98
c 4 9 24
d 8 27 11
e 16 81 64
Copied! | |
pandas_series/dataframe-indexing.html6_6_0.txt | .rst .pdf
Contents | |
pandas_series/pandas_series_127_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
* [ pandas.Series.equals ](pandas.Series.equals.html)
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... | |
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pandas_series/pandas_series_298_6.txt | ion 2.1: ` last() ` is deprecated and will be removed
in a future version. Please create a mask and filter using .loc instead.
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
... | |
pandas_series/pandas_series_32_3.txt | ml)
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pandas_series/pandas_series_138_5.txt | (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_156_4.txt | das.Series.str.capitalize.html)
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* [ pandas.Series.... | |
pandas_series/pandas-settingwithcopywarning0_82_0.txt | Copied!
At first, the view and copy of ` df ` look the same. If you compare their
NumPy representations, though, then you may notice this subtle difference: | |
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pandas_series/pandas_series_287_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
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* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_338_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
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pandas_series/pandas-settingwithcopywarning0_175_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/pandas_series_235_3.txt | ml)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
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pandas_series/pandas-settingwithcopywarning0_108_0.txt | Now that you have all these arrays, let’s see what happens when you alter the
original:
Python | |
pandas_series/pandas_series_79_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
* [ pandas.Series.str.casefold ](pandas.Series.str.casefold.html)
* [ pandas.Series.str.cat ](pandas.Series.str.cat.html)
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pandas_series/pandas_series_30_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
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* [ pandas.Serie... | |
pandas_series/pandas_series_10_6.txt |
>>> ser = pd.Series(pd.to_timedelta([1, 2, 3], unit='ns'))
>>> ser
0 0 days 00:00:00.000000001
1 0 days 00:00:00.000000002
2 0 days 00:00:00.000000003
dtype: timedelta64[ns]
>>> ser.dt.nanoseconds
0 1
1 2
2 3
dtype: int32
For TimedeltaIndex:
... | |
pandas_series/pandas-settingwithcopywarning0_115_0.txt | This is essentially the same behavior that produces a ` SettingWithCopyWarning
` in pandas, but that warning doesn’t exist in NumPy.
#### Multidimensional Arrays | |
pandas_series/pandas_series_164_2.txt | vel.html)
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... | |
pandas_series/pandas_series_196_2.txt | vel.html)
* [ pandas.Series.drop_duplicates ](pandas.Series.drop_duplicates.html)
* [ pandas.Series.duplicated ](pandas.Series.duplicated.html)
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... | |
pandas_series/pandas_series_228_5.txt | as.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_categories.html)
* [ pandas.Series.... | |
pandas_series/pandas_series_164_6.txt | _
nan_rep = None _ , _ dropna = None _ , _ data_columns = None _ , _
errors = 'strict' _ , _ encoding = 'UTF-8' _ ) [ [source]
](https://github.com/pandas-
dev/pandas/blob/v2.2.1/pandas/core/generic.py#L2716-L2868) #
Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Form... | |
pandas_series/pandas_series_310_6.txt | rical 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 function [ ` pandas.factorize() `
](pandas.factorize.html#pandas.factorize "pandas.factorize") , and as a method
` Series.factoriz... | |
pandas_series/pandas_series_208_3.txt | .sparse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
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* [ pandas.Series.dt.timetz ](pandas.Series.dt.timetz.html)
* [ pandas.Series.dt.year ](pandas.Series.... | |
pandas_series/pandas_series_58_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_319_6.txt | c.py#L11078-L11230) #
Shift index by desired number of periods with an optional time freq .
When freq is not passed, shift the index without realigning the data. If
freq is passed (in this case, the index must be date or datetime, or it will
raise a NotImplementedError ), the index will be increased usin... | |
pandas_series/pandas_series_56_5.txt | das.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... |
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