. . . . . . . . . . . . . . . . . . 115 10 Group By: split-apply-combine 117 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 10.2 improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (GH496) • Can store objects indexed by tuples and floats in HDFStore (GH492) • Don’t faster in many cases) to scalar elements (GH437, GH438). set_value is capable of producing an enlarged object. • Add PyQt table widget to sandbox (PR435) • DataFrame.align can accept Series arguments and an

. . . . . . . . . . . . . . . . . . 115 10 Group By: split-apply-combine 117 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 10.2 improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (GH496) • Can store objects indexed by tuples and floats in HDFStore (GH492) • Don’t faster in many cases) to scalar elements (GH437, GH438). set_value is capable of producing an enlarged object. • Add PyQt table widget to sandbox (PR435) • DataFrame.align can accept Series arguments and an

Customarily, we import as 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 [11]: df2.dtypes Out[11]: A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object If youre using IPython, tab completion for column names (as well as public attributes) is dtype='datetime64[ns]', freq='D') In [16]: df.columns Out[16]: Index(['A', 'B', 'C', 'D'], dtype='object') DataFrame.to_numpy() gives a NumPy representation of the underlying data. Note that this can be

. . . . . . . . . . . . . . . . . . 123 10 Group By: split-apply-combine 125 10.1 Splitting an object into groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 10.2 improve performance of multi-GroupBy aggregation when a Python function is passed, reuse ndarray object in Cython (GH496) • Can store objects indexed by tuples and floats in HDFStore (GH492) • Don’t faster in many cases) to scalar elements (GH437, GH438). set_value is capable of producing an enlarged object. • Add PyQt table widget to sandbox (PR435) • DataFrame.align can accept Series arguments and an

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2.1.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 2.12.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 2.16.1 Styler Object and HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748 2.16.2 Formatting

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 2.1.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 2.12.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 2.16.1 Styler Object and HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 2.16.2 Formatting

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2.1.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635 2.12.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 636 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 2.16.1 Styler Object and HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 2.16.2 Formatting

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2.1.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 2.12.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 2.16.1 Styler Object and HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 2.16.2 Formatting

pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2.1.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 2.12.1 Object creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 638 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 2.16.1 Styler Object and HTML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 751 2.16.2 Formatting

package and dependency updates. You can find simple installation instructions for pandas in this document: installation instructions . Installing from source See the contributing Survived int64 Pclass int64 Name object Sex object Age float64 SibSp int64 Parch int64 Ticket object Fare float64 Cabin object Embarked object dtype: object For each of the columns, the used is enlisted. The data types in this DataFrame are integers (int64), floats (float64) and strings (object). Note: When asking for the dtypes, no brackets are used! dtypes is an attribute of a DataFrame