【Pythonプログラミング】Pandasの基本 (後編) 〜 表形式データ・データ分析 〜

Pythonプログラミングに関する情報を発信しているサプーです! 
この動画はPythonのPandasのライブラリの使い方について解説しています。表形式データ(テーブルデータ)の操作やデータ分析、機械学習の分野でもよく使われるPandasのDataFrameの操作の基本について学べます😊

・DataFrameで集計
・DataFrameの結合
・DataFrameとラムダ式
・グラフの描画
これらについて説明しています!!

💻 動作環境 💻
Windows 10
Python 3.9.0

面白いと思ったらいいねボタン・チャンネル登録をしてくれたら、とってもとっても嬉しいです💕 一緒にプログラミングを勉強していきましょう!!

⭐️ チャプター ⭐️
0:00 今日のテーマ「Pandasの基本 (DataFrame操作)」
0:38 GroupByによる集計
6:10 index・columnsの変更
11:05 concatによる結合
15:12 mergeによる結合
20:47 map + ラムダ式
23:44  グラフ描画
25:57 エンディング

#python 
#pandas 
#dataframe 
#programming 

【Pythonプログラミング】Pandasの基本 (後編)  〜 表形式データ・データ分析 〜
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First I have shown you how you can apply isnull function to return true or false in each cell to indicate whether the cell is having null value or not.


After that I have shown you how you can apply sum function that will summarize the null values for all of the columns to indicate how many missing values are present in each column.


After that I have shown you how you can view the missing values in a specific column in case we are interested in viewing missing value in a specific column and removing it.


After viewing the null values, I have shown how can drop the null values using dropna function and specify the parameters how to indicate whether we want to remove vales in a row or column that is having a single missing values or entire row is having missing value. This option is useful to specify condition based on our needs of dropping null values.

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After that I shown you how you can specify multiple columns for row or column like we usually do to create nested pivot table.


Sometimes we are not able to recall the columns present in pandas dataframe to specify in pivot table, therefore, I have also shown you how you can display the columns of pandas dataframe.


After that I have shown you how you can specify the values that needs to come for index as with the help of index you can easily navigate the dataframe or pivot table,


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