pandas the Python and Data Analysis Library (Spyder 5 IDE)

Channel:
Philip Yip
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Published on ● Video Link: https://www.youtube.com/watch?v=9maqRLD3KWw


Duration: 1:30:03
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pandas, the Python and Data Analysis library is based around the dataframe, series and index data structures. These data structure are defined by their classes DataFrame, Series and Index respectively. An index is normally a numeric array, usually in the form of a numpy arange object however it can also be a list of strings. A series is a numpy array, where every value is associated with a corresponding index and the series in the form of a column also has a column name. A dataframe is a collection of series which each have a common index. A dataframe is essentially analogous to an Excel worksheet and is one of the most commonly used data structures for data sciences.

It is advised to install Anaconda and familiarise yourself with the Spyder 5 IDE, Python Procedural Programming, Python Code Blocks, Python Object Orientated Programming and numpy (as the data structures in pandas are built upon numpy arrays) before proceeding.

Written Guides:
https://dellwindowsreinstallationguide.com/python/
Python Playlist:
https://www.youtube.com/playlist?list=PL1RkaknDn7v-EO4V38oDBkZNd6_530nan

0:00:58 Conceptualising a dataframe
0:03:09 Python dictionary
0:04:38 Importing the data science libraries
0:06:42 Object Orientated Programming Recap
0:14:06 Instantiating a dataframe
0:17:28 Indexing
0:28:20 Renaming column names and index names
0:33:47 inplace (depreciated keyword input argument)
0:36:05 Dropping a column or observation
0:36:36 Axis 0 index and 1 columns
0:38:09 reset index
0:38:49 Inserting a column
0:40:08 Appending and Concatenation
0:43:40 Appending a new observation
0:44:52 Comma Separated Values (CSV) and Tab Delimited (TXT) Files
0:47:04 Reading a CSV File or a TXT File
0:54:38 Reading an Excel XLSX File
0:56:18 Writing a dataframe to File
0:58:16 Writing dataframes to Sheets within an Excel File
1:01:10 Indexing using Boolean Values
1:05:18 Handling Missing Data
1:10:16 Sorting Data
1:13:02 Categorical Data
1:20:43 Group By a Category
1:26:06 Date and Time

#pandas #Python #Spyder



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