id stringlengths 16 145 | text stringlengths 1 179k | title stringclasses 1
value |
|---|---|---|
pandas_series/pandas_series_183_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)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_181_6.txt | ) #
Replace values where the condition is False.
Parameters :
**cond** bool Series/DataFrame, array-like, or callable
Where cond is True, keep the original value. Where False, replace with
corresponding value from other . If cond is callable, it is computed on
the Series/DataFrame and shoul... | |
pandas_series/pandas-settingwithcopywarning0_39_0.txt | Here’s what happens in the code sample above:
* ` df[mask] ` returns a completely new DataFrame (outlined in purple). This DataFrame holds a copy of the data from ` df ` that correspond to ` True ` values from ` mask ` (highlighted in green).
* ` df[mask]["z"] = 0 ` modifies the column ` z ` of the new DataFrame ... | |
pandas_series/pandas_series_246_4.txt | as.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.Series.s... | |
pandas_series/pandas_series_226_5.txt | s.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.c... | |
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pandas_series/pandas_series_199_2.txt | ries.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.idxma... | |
pandas_series/pandas-settingwithcopywarning0_176_0.txt | Copied!
Here, ` df["powers"] ` returns a DataFrame with the columns ` x ` and ` y ` .
This is just a view that points to the data from ` df ` , so the assignment is
successful and ` df ` is modified. But pandas still issues a `
SettingWithCopyWarning ` . | |
pandas_series/pandas_series_65_6.txt | um 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** {index (0)}
Axis for the function to be applied on. For Series this parameter is unused
and defaults to 0.
F... | |
pandas_series/pandas_series_61_6.txt | her.
Parameters :
**tz** str, pytz.timezone, dateutil.tz.tzfile, datetime.tzinfo or None
Time zone for time. Corresponding timestamps would be converted to this time
zone of the Datetime Array/Index. A tz of None will convert to UTC and
remove the timezone information.
Returns :
Array or Inde... | |
pandas_series/pandas_series_265_4.txt | ndas.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.Series... | |
pandas_series/pandas_series_279_6.txt |
Return an object with matching indices as other object.
Conform the object to the same index on all axes. Optional filling logic,
placing NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
copy=False.
Parameters :
*... | |
pandas_series/pandas_series_72_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)
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pandas_series/pandas_series_186_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_series_188_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)
* [ pandas.Seri... | |
pandas_series/pandas_series_299_8.txt | ement is True, potentially over an axis.
[ ` apply ` ](pandas.Series.apply.html#pandas.Series.apply "pandas.Series.apply") (func[, convert_dtype, args, by_row]) | Invoke function on values of Series.
[ ` argmax ` ](pandas.Series.argmax.html#pandas.Series.argmax "pandas.Series.argmax") ([axis, skipna]) | ... | |
pandas_series/pandas_series_336_4.txt | andas.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.Serie... | |
pandas_series/pandas_series_136_2.txt | ies.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.idxmax... | |
pandas_series/pandas_series_39_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)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_297_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)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas_series_76_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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Ser... | |
pandas_series/pandas_series_62_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)
* [ pandas.Series.str.center ](pandas.Series.str.center.html)
* [ pandas.Series.str.contains ](pandas.Series.str.conta... | |
pandas_series/pandas-settingwithcopywarning0_172_0.txt | You can use this approach to modify the elements of DataFrames with
hierarchical indices:
Python | |
pandas_series/pandas_series_223_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)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas-settingwithcopywarning0_62_0.txt | Python
>>> view_of_arr = arr.view()
>>> view_of_arr
array([ 1, 2, 4, 8, 16, 32])
>>> view_of_arr.base
array([ 1, 2, 4, 8, 16, 32])
>>> view_of_arr.base is arr
True
| |
pandas_series/pandas_series_88_5.txt | ries.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.cat.r... | |
pandas_series/pandas_series_206_6.txt | _names = True _ , _ bold_rows = False _ , _ column_format =
None _ , _ longtable = None _ , _ escape = None _ , _ encoding = None
_ , _ decimal = '.' _ , _ multicolumn = None _ , _ multicolumn_format =
None _ , _ multirow = None _ , _ caption = None _ , _ label = None _
, _ position = ... | |
pandas_series/pandas_series_19_7.txt |
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pandas_series/pandas_series_54_5.txt | .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.ca... | |
pandas_series/pandas_series_250_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.d... | |
pandas_series/pandas-settingwithcopywarning0_186_0.txt | Copied!
In addition to modifying the default behavior, you can use ` get_option() `
to retrieve the current setting related to ` mode.chained_assignment ` : | |
pandas_series/pandas_series_341_4.txt | es.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.Series.str.coun... | |
pandas_series/pandas_series_224_6.txt | ata** Series or CategoricalIndex
Examples
>>> s = pd.Series(list("abbccc")).astype("category")
>>> s
0 a
1 b
2 b
3 c
4 c
5 c
dtype: category
Categories (3, object): ['a', 'b', 'c']
>>> s.cat.categories
Index(['a', 'b', 'c... | |
pandas_series/pandas_series_303_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)
* [ pandas.Series.cat.remove_categories ](pandas.Series.cat.remove_categories.html)
* [ pandas.Series... | |
pandas_series/pandas_series_173_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.d... | |
pandas_series/pandas_series_262_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)
* [ pandas.Series.first ](pandas.Series.first.html)
* [ pandas.Series.head ](pandas.Series.head.html)
... | |
pandas_series/pandas_series_279_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_314_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_series_273_2.txt | vel.html)
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... | |
pandas_series/pandas_series_180_2.txt | ries.drop_duplicates ](pandas.Series.drop_duplicates.html)
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* [ pandas.Series.equals ](pandas.Series.equals.html)
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pandas_series/pandas-settingwithcopywarning0_129_0.txt | Copied!
This view looks at the same data as ` df ` . | |
pandas_series/pandas_series_324_1.txt | html)
* [ pandas.Series.rtruediv ](pandas.Series.rtruediv.html)
* [ pandas.Series.rfloordiv ](pandas.Series.rfloordiv.html)
* [ pandas.Series.rmod ](pandas.Series.rmod.html)
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* [ pandas.Series.combine ](pandas.Series.combine.html)
* [ pandas.Serie... | |
pandas_series/pandas_series_133_6.txt | e option ` plotting.backend ` . By default,
matplotlib is used.
Parameters :
**data** Series or DataFrame
The object for which the method is called.
**x** label or position, default None
Only used if data is a DataFrame.
**y** label, position or list of label, positions, default None
Al... | |
pandas_series/pandas_series_19_3.txt | ml)
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pandas_series/pandas_series_337_4.txt | ndas.Series.str.capitalize ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_43_6.txt | generic.py#L11534-L11726) #
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a timezone-naive
Series, use [ ` Series.dt.tz_localize() `
](pandas.Series.dt.tz_localize.html#pandas.Series.dt.tz_localize
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pandas_series/pandas_series_254_6.txt | 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 In... | |
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pandas_series/pandas_series_175_6.txt | eters :
**order** list of int representing new level order
Reference level by number or key.
Returns :
type of caller (new object)
Examples
>>> arrays = [np.array(["dog", "dog", "cat", "cat", "bird", "bird"]),
... np.array(["white", "black", "white", "black", ... | |
pandas_series/pandas_series_296_3.txt | ml)
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pandas_series/pandas_series_15_3.txt | rse ](pandas.DataFrame.sparse.html)
* [ pandas.Index.str ](pandas.Index.str.html)
* [ pandas.Series.dt.date ](pandas.Series.dt.date.html)
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pandas_series/pandas_series_328_5.txt | as.Series.cat.rename_categories.html)
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pandas_series/pandas_series_162_5.txt | andas.Series.cat.rename_categories.html)
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* [ pandas.Series.cat.add_categories ](pandas.Series.cat.add_categories.html)
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pandas_series/pandas_series_305_2.txt | ies.drop_duplicates ](pandas.Series.drop_duplicates.html)
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pandas_series/pandas_series_168_5.txt | s.Series.cat.rename_categories.html)
* [ pandas.Series.cat.reorder_categories ](pandas.Series.cat.reorder_categories.html)
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pandas_series/pandas_series_246_5.txt | s.Series.cat.rename_categories.html)
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pandas_series/pandas_series_216_4.txt | andas.Series.str.capitalize.html)
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pandas_series/pandas_series_280_5.txt | ](pandas.Series.cat.rename_categories.html)
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pandas_series/pandas_series_153_3.txt | ml)
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pandas_series/pandas_series_121_3.txt | ml)
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pandas_series/pandas_series_284_4.txt | ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_139_6.txt | 2.2.0: Returning a tuple from a callable is
deprecated.
` .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 slice o... | |
pandas_series/pandas_series_201_1.txt | html)
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pandas_series/pandas_series_224_4.txt | ](pandas.Series.str.capitalize.html)
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pandas_series/pandas_series_283_1.txt | html)
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pandas_series/pandas_series_165_4.txt | .Series.str.capitalize.html)
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pandas_series/pandas_series_339_5.txt | das.Series.cat.rename_categories.html)
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pandas_series/pandas_series_156_1.txt | html)
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pandas_series/pandas_series_244_3.txt | e.sparse ](pandas.DataFrame.sparse.html)
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pandas_series/pandas_series_79_6.txt | eries
The data type of each child field.
Examples
>>> import pyarrow as pa
>>> s = pd.Series(
... [
... {"version": 1, "project": "pandas"},
... {"version": 2, "project": "pandas"},
... {"version": 1, "project": "numpy"},
... ],
... ... | |
pandas_series/pandas_series_250_4.txt | pandas.Series.str.capitalize.html)
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pandas_series/pandas-settingwithcopywarning0_151_0.txt |
>>> 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
Copied! | |
pandas_series/pandas_series_59_3.txt | ml)
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pandas_series/pandas-settingwithcopywarning0_193_0.txt | 🐍 Python Tricks 💌
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pandas_series/pandas_series_315_1.txt | html)
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* [ pandas.Serie... | |
pandas_series/pandas_series_262_1.txt | html)
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pandas_series/pandas_series_172_3.txt | ml)
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* [ pandas.Ser... | |
pandas_series/pandas_series_288_6.txt | e last/highest place in the categories
and will be unused directly after this call.
Parameters :
**new_categories** category or list-like of category
The new categories to be included.
Returns :
Categorical
Categorical with new categories added.
Raises :
ValueError
If the... | |
pandas_series/pandas_series_109_5.txt | 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.cat... | |
pandas_series/pandas_series_98_6.txt | d skew over requested axis.
Normalized by N-1.
Parameters :
**axis** {index (0)}
Axis for the function to be applied on. For Series this parameter is unused
and defaults to 0.
For DataFrames, specifying ` axis=None ` will apply the aggregation across
both axes.
New in version 2.0.0.
**skipna** bo... | |
pandas_series/pandas_series_191_2.txt | vel.html)
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... |
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