PythonのPandasのDataFrameの扱い方 #Python

PythonのPandasのDataFrameでデータを柔軟に扱いたかったので、個別に扱う方法を調べた。

csvデータ

ソースコード

animedata.py

import sys
import pandas as pd 

args = sys.argv
file_name = args[1]

print(file_name)
print(type(file_name))

anime_datalist = pd.read_csv(file_name)
print(anime_datalist.head())
print(anime_datalist.columns)

row_count = 0
all_row_num = len(anime_datalist)
print(all_row_num)

print(anime_datalist['Title'][1])

while row_count < 50:
    print(anime_datalist['Title'][row_count])
    row_count = row_count + 1

まず、データを
anime_datalist.head()
で先頭から5行までを表示し、行名と列名を確認。
このデータだと行は数値で表せる事がわかる。

                              Title   Type Episodes            Status  ... Scored by  Members Favorites                                        Description
0  Fullmetal Alchemist: Brotherhood     TV       64   Finished Airing  ...    719706  1176368    105387  "In order for something to be obtained, someth...
1                    Kimi no Na wa.  Movie        1   Finished Airing  ...    454969   705186     33936  Mitsuha Miyamizu, a high school girl, yearns t...
2                          Gintama°     TV       51   Finished Airing  ...     70279   194359      5597  Gintoki, Shinpachi, and Kagura return as the f...
3                     Steins;Gate 0     TV       23  Currently Airing  ...     12609   186331      1117  The dark untold story of Steins;Gate that lead...
4                       Steins;Gate     TV       24   Finished Airing  ...    552791   990419     90365  The self-proclaimed mad scientist Rintarou Oka...

列名を詳しく知りたいので、
anime_datalist.columns
で確認。

Index(['Title', 'Type', 'Episodes', 'Status', 'Start airing', 'End airing',
       'Starting season', 'Broadcast time', 'Producers', 'Licensors',
       'Studios', 'Sources', 'Genres', 'Duration', 'Rating', 'Score',
       'Scored by', 'Members', 'Favorites', 'Description'],
      dtype='object')

ここまで調べれば、そのまま2次元配列として扱えるので、

print(anime_datalist['Title'][0])

と言う直接指定や

while row_count < 50:
    print(anime_datalist['Title'][row_count])
    row_count = row_count + 1

などのように個別に扱う事ができる。