Pandas Concepts: Difference between revisions
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=Index= | =Index= | ||
{{External|https://pandas.pydata.org/docs/reference/indexing.html}} | |||
==RangeIndex== | |||
<code>RangeIndex(start=0, stop=3, step=1)</code> | |||
==Time Series Index== | |||
A time series is a series whose index has [[Pandas_Concepts#Datetime|datetime]] objects. To create a time series, ensure that the method that creates the series performs the conversion automatically, as show in the [[#Create_a_Time_Series_from_CSV|Create a Time Series from CSV]] section. | |||
=Data Types= | =Data Types= |
Revision as of 18:48, 8 October 2023
Internal
Overview
pandas
is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language.
Pandas is built in top of Numpy.
DataFrame
Series
Axis
Both DataFrames and Series use the concept of axis. Formally define. By default, an axis comprises of monotonically increasing integers with step 1, from 0 to length - 1
Index
RangeIndex
RangeIndex(start=0, stop=3, step=1)
Time Series Index
A time series is a series whose index has datetime objects. To create a time series, ensure that the method that creates the series performs the conversion automatically, as show in the Create a Time Series from CSV section.
Data Types
String
Datetime
TO PROCESS: https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.html#pandas.Timestamp
Reported as datetime64[ns]
. What is this?
Also see