argument. When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple of (function, keyword) indicating where the DataFrame should flow. For example: In [1]: import sample(n=1, weights=example_weights2) Out[23]: 0 0 dtype: int64 When applied to a DataFrame, one may pass the name of a column to specify sampling weights when sampling from rows. In [24]: df = DataFrame({'col1':[9 6 1.4 0.2 Iris-setosa 0.720000 Above was an example of inserting a precomputed value. We can also pass in a function to be evalutated. In [4]: iris.assign(sepal_ratio = lambda x: (x['SepalWidth'] /

above each subplot if suplots=True and title is a list of strings (GH14753) • DataFrame.plot can pass the matplotlib 2.0 default color cycle as a single string as color parameter, see here. (GH15516) set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; You can now pass a list of color=[color_negative, color_positive]. (GH14757) 1.3.2 Backwards incompatible API changes to_datetime(df) Out[31]: 0 2015-02-04 02:00:00 1 2016-03-05 03:00:00 dtype: datetime64[ns] You can pass only the columns that you need to assemble. In [32]: pd.to_datetime(df[['year', 'month', 'day']])

to_datetime(df) Out[31]: 0 2015-02-04 02:00:00 1 2016-03-05 03:00:00 dtype: datetime64[ns] You can pass only the columns that you need to assemble. In [32]: pd.to_datetime(df[['year', 'month', 'day']]) In the new API, you can either downsample OR upsample. The prior implementation would allow you to pass an aggregator function (like mean) even though you were upsampling, providing a bit of confusion. argument. When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple of (function,keyword) indicating where the DataFrame should flow. For example: In [1]: import

to_datetime(df) Out[31]: 0 2015-02-04 02:00:00 1 2016-03-05 03:00:00 dtype: datetime64[ns] You can pass only the columns that you need to assemble. In [32]: pd.to_datetime(df[['year', 'month', 'day']]) In the new API, you can either downsample OR upsample. The prior implementation would allow you to pass an aggregator function (like mean) even though you were upsampling, providing a bit of confusion. argument. When the function you wish to apply takes its data anywhere other than the first argument, pass a tuple of (function,keyword) indicating where the DataFrame should flow. For example: In [1]: import

above each subplot if suplots=True and title is a list of strings (GH14753) • DataFrame.plot can pass the matplotlib 2.0 default color cycle as a single string as color parameter, see here. (GH15516) set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; You can now pass a list of color=[color_negative, color_positive]. (GH14757) 20 Chapter 1. What’s New pandas: powerful to_datetime(df) Out[31]: 0 2015-02-04 02:00:00 1 2016-03-05 03:00:00 dtype: datetime64[ns] You can pass only the columns that you need to assemble. In [32]: pd.to_datetime(df[['year', 'month', 'day']])

above each subplot if suplots=True and title is a list of strings (GH14753) • DataFrame.plot can pass the matplotlib 2.0 default color cycle as a single string as color parameter, see here. (GH15516) set with align='left'|'mid'|'zero', the default is “left”, which is backward compatible; You can now pass a list of color=[color_negative, color_positive]. (GH14757) 1.5.2 Backwards incompatible API changes to_datetime(df) Out[31]: 0 2015-02-04 02:00:00 1 2016-03-05 03:00:00 dtype: datetime64[ns] You can pass only the columns that you need to assemble. In [32]: pd.to_datetime(df[['year', 'month', 'day']])

min_height max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75 [2 rows x 3 columns] Pass the desired columns names as the **kwargs to .agg. The values of **kwargs should be tuples where the representations of Pandas objects are now generally defined in __repr__, and calls to __str__ in general now pass the call on to the __repr__, if a specific __str__ method doesn’t exist, as is standard for Python use the head() and tail() methods. The default number of elements to display is five, but you may pass a custom number. In [4]: long_series = pd.Series(np.random.randn(1000)) In [5]: long_series.head()

min_height max_height average_weight kind cat 9.1 9.5 8.90 dog 6.0 34.0 102.75 [2 rows x 3 columns] Pass the desired columns names as the **kwargs to .agg. The values of **kwargs should be tuples where the representations of Pandas objects are now generally defined in __repr__, and calls to __str__ in general now pass the call on to the __repr__, if a specific __str__ method doesn’t exist, as is standard for Python use the head() and tail() methods. The default number of elements to display is five, but you may pass a custom number. In [4]: long_series = pd.Series(np.random.randn(1000)) In [5]: long_series.head()

register_matplotlib_converters() (GH18307) • Series.plot() no longer accepts positional arguments, pass keyword arguments instead (GH30003) • DataFrame.hist() and Series.hist() no longer allows figsize="default" instead (GH18836) • read_excel() no longer allows an integer value for the parameter usecols, instead pass a list of integers from 0 to usecols inclusive (GH23635) • Removed the previously deprecated keyword instead (GH24048) • Passing floating dtype codes to Categorical.from_codes() is no longer supported, pass codes. astype(np.int64) instead (GH21775) • Removed the previously deprecated keyword “pat” from

"a\r\nbc"\r\n' For file objects, specifying newline is not sufficient to set the line terminator. You must pass in the line_terminator explicitly, even in this case. In [1]: data = pd.DataFrame({"string_with_lf": pyarrow>=0.11.0. (GH23053) • pandas.read_excel() has deprecated accepting usecols as an integer. Please pass in a list of ints from 0 to usecols inclusive instead (GH23527) • Constructing a TimedeltaIndex from 'pandas.Timestamp' with the correct ˓→'tz'. To accept the future behavior, pass 'dtype=object'. To keep the old behavior, pass 'dtype="datetime64[ns]"'. #!/bin/python3 Out[8]: array(['1999-12-31T23:00:00