\\\\\\\\\\\\\\\\\\Out[5]: ˓→ 1 2 2 NaN Length: 2, dtype: Int64 # operate with other dtypes In [6]: s + s.iloc[1:3].astype('Int8') \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[6]: ˓→ 0 NaN 1 4 2 NaN Length: 3, dtype: Int64 # coerce when needed In [7]: s + 0.01 \\\\\\\\\\\ pd.Series([1, 2, 3]) In [24]: ser.array Out[24]: [1, 2, 3] (continues on next page) 6 Chapter 1. What’s New in 0.24.0 (January 25, 2019) pandas: powerful Python data analysis toolkit, Release

pandas.core.groupby.GroupBy. agg (GH26430). In [6]: animals.groupby('kind').height.agg([ ...: lambda x: x.iloc[0], lambda x: x.iloc[-1] ...: ]) ...: Out[6]: kind cat 9.1 9.5 dog Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a', 2), ( 'a', 3), ( 'a', 4), ( 'a', 5), ( 'a', 6), ( 'a', 7), (continues on next page) 1.1. Enhancements 5 pandas: powerful Python data analysis 'CreatedBy': {'Name': 'User001'}, ....: 'Lookup': {'TextField': 'Some text', (continues on next page) 6 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit, Release

pandas.core.groupby.GroupBy. agg (GH26430). In [6]: animals.groupby('kind').height.agg([ ...: lambda x: x.iloc[0], lambda x: x.iloc[-1] ...: ]) ...: Out[6]: kind cat 9.1 9.5 dog Out[8]: MultiIndex([( 'a', 0), ( 'a', 1), ( 'a', 2), ( 'a', 3), ( 'a', 4), ( 'a', 5), ( 'a', 6), ( 'a', 7), ( 'a', 8), (continues on next page) 1.1. Enhancements 5 pandas: powerful Python data {'TextField': 'Some text', ....: 'UserField': {'Id': 'ID001', 'Name': 'Name001'}}, (continues on next page) 6 Chapter 1. What’s new in 0.25.0 (July 18, 2019) pandas: powerful Python data analysis toolkit, Release

your distribution. To install pandas for Python 2, you may need to use the python-pandas package. 6 Chapter 2. Installation pandas: powerful Python data analysis toolkit, Release 0.25.3 Distribution of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame data analysis toolkit, Release 0.25.3 In [5]: dates = pd.date_range('20130101', periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06']

0.0 (January 29, 2020) pandas: powerful Python data analysis toolkit, Release 1.0.0 In [6]: np.nan > 1 Out[6]: False In [7]: pd.NA > 1 Out[7]: For logical operations, pd.NA follows the rules of False, None], dtype="boolean") In [17]: s Out[17]: 0 True 1 False 2 Length: 3, dtype: boolean 6 Chapter 1. What’s new in 1.0.0 (January 29, 2020) pandas: powerful Python data analysis toolkit, Release of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame

Calendar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2374 6 Release Notes 2375 6.1 Version 1.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . tools for working with dates, times, and time- indexed data. To introduction tutorial To user guide 6 Chapter 2. Getting started pandas: powerful Python data analysis toolkit, Release 1.0.3 Straight to of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame

then install pip, and then use pip to install those packages: conda install pip pip install django 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.1.1 Installing create a Series from scratch as well: In [5]: ages = pd.Series([22, 35, 58], name="Age") In [6]: ages Out[6]: 0 22 1 35 2 58 Name: Age, dtype: int64 1.4. Tutorials 15 pandas: powerful Python data 4 5 0 3 Allen, Mr. William Henry ˓→ male ... 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James ˓→ male ... 0 330877 8.4583 NaN Q 6 7 0 1 McCarthy, Mr. Timothy J ˓→ male ... 0 17463 51.8625 E46 S 7 8

then install pip, and then use pip to install those packages: conda install pip pip install django 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.1.0 Installing create a Series from scratch as well: In [5]: ages = pd.Series([22, 35, 58], name="Age") In [6]: ages Out[6]: 0 22 1 35 2 58 Name: Age, dtype: int64 1.4. Tutorials 15 pandas: powerful Python data 0 3 Allen, Mr. William Henry ˓→ male 35.0 0 0 373450 8.0500 NaN S 5 6 0 3 Moran, Mr. James ˓→ male NaN 0 0 330877 8.4583 NaN Q 6 7 0 1 McCarthy, Mr. Timothy J ˓→ male 54.0 0 0 17463 51.8625 E46 S

. . . . . . . . . . . . . . . . . . . . . . 6 1.4.1 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.2 Package overview . . . . . . . . . . . self contained Python installation, and then use the Conda command to install additional packages. 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.3.2 First you create a Series from scratch as well: In [5]: ages = pd.Series([22, 35, 58], name="Age") In [6]: ages Out[6]: 0 22 1 35 2 58 Name: Age, dtype: int64 A pandas Series has no column labels, as it is just

Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4.2 Package overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . minimal environment with only Python installed in it. To put your self inside this environment run: 6 Chapter 1. Getting started pandas: powerful Python data analysis toolkit, Release 1.3.3 source activate create a Series from scratch as well: In [5]: ages = pd.Series([22, 35, 58], name="Age") In [6]: ages Out[6]: 0 22 1 35 2 58 Name: Age, dtype: int64 A pandas Series has no column labels, as it is just