. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 . . . . . . . . . . . . 1635 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1636 34.12.1.4 pandas.core.window.Rolling.median . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144 . . . . . . . . . . . . 1766 34.12.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1766 34.12.1.4 pandas.core.window.Rolling.median . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 30.6 Out-of-core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 . . . . . . . . . . . . 1903 34.15.1.1 pandas.core.window.Rolling.count . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.2 pandas.core.window.Rolling.sum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.3 pandas.core.window.Rolling.mean . . . . . . . . . . . . . . . . . . . . . . . . . . 1904 34.15.1.4 pandas.core.window.Rolling.median . . . . . . . . . . . . .

(GH12004) • read_stata() can read Stata 119 dta files. (GH28250) • Implemented pandas.core.window.Window.var() and pandas.core.window.Window. std() functions (GH26597) • Added encoding argument to DataFrame in ----> 1 mi.levels[0].name = "new name" /pandas/pandas/core/indexes/base.py in name(self, value) 1189 # Used in MultiIndex.levels to avoid silently ignoring "text_col": ["a", "b", "c"], ... "float_col": [0.0, 0.1, 0.2]}) >>> df.info(verbose=True) core.frame.DataFrame'> RangeIndex: 3 entries, 0 to 2 Data columns (total 3 columns): int_col 3 non-null

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo pandas: powerful Python data analysis toolkit, Release 1.0.5 In [9]: titanic.info() core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: 44 Chapter 1. Getting started pandas:

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo NaN S I’m interested in a technical summary of a DataFrame In [9]: titanic.info() core.frame.DataFrame'> (continues on next page) 42 Chapter 1. Getting started pandas: powerful Python We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: 44 Chapter 1. Getting started pandas:

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo NaN S I’m interested in a technical summary of a DataFrame In [9]: titanic.info() core.frame.DataFrame'> (continues on next page) 44 Chapter 2. Getting started pandas: powerful Python We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: 46 Chapter 2. Getting started pandas:

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: In [7]: titanic["Age"].shape Out[7]: (891

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo NaN S I’m interested in a technical summary of a DataFrame In [9]: titanic.info() core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: In [7]: titanic["Age"].shape Out[7]: (891

entities. Wes McKinney is the Benevolent Dictator for Life (BDFL). Development team The list of the Core Team members and more detailed information can be found on the people’s page of the governance repo columns] I’m interested in a technical summary of a DataFrame In [9]: titanic.info() core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null We can verify this by checking the type of the output: In [6]: type(titanic["Age"]) Out[6]: pandas.core.series.Series And have a look at the shape of the output: In [7]: titanic["Age"].shape Out[7]: (891