statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures follows: In [1]: import numpy as np In [2]: import pandas as pd 3.2.1 Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer

statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) rolling_window(), and rolling_apply() with center=True previously would return a result of the same structure as the input arg with NaN in the final (window-1)/2 entries. 1.3. v0.15.0 (October 18, 2014) 23 com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure of the unlocalized data (GH4230), see the docs • DatetimeIndex is now in the API documentation

statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) rolling_window(), and rolling_apply() with center=True previously would return a result of the same structure as the input arg with NaN in the final (window-1)/2 entries. Now the final (window-1)/2 entries com.load_data(’Titanic’) • tz_localize can infer a fall daylight savings transition based on the structure of the unlocalized data (GH4230), see the docs • DatetimeIndex is now in the API documentation

statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures follows: In [1]: import numpy as np In [2]: import pandas as pd 2.3.1 Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer

statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) rolling_window(), and rolling_apply() with center=True previously would return a result of the same structure as the input arg with NaN in the final (window-1)/2 entries. Now the final (window-1)/2 entries com.load_data('Titanic') • tz_localize can infer a fall daylight savings transition based on the structure of the unlocalized data (GH4230), see the docs • DatetimeIndex is now in the API documentation

split-apply-combine approach. To introduction tutorial To user guide Straight to tutorial... Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures

split-apply-combine approach. To introduction tutorial To user guide Straight to tutorial... Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures

split-apply-combine approach. To introduction tutorial To user guide Straight to tutorial... Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures

split-apply-combine approach. To introduction tutorial To user guide Straight to tutorial... Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures

split-apply-combine approach. To introduction tutorial To user guide Straight to tutorial... Change the structure of your data table in multiple ways. You can melt() your data table from wide to long/tidy form statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional) array 2 DataFrame General 2D labeled, size-mutable tabular structure with potentially heterogeneously-typed column Why more than one data structure? The best way to think about the pandas data structures