Mari menulis dan memahami Pohon Keputusan dengan Python dari awal! Bagian 3. Pustaka Analisis Data Pandas

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Pada artikel ini, saya akan menjelaskan cara bekerja dengan pustaka Pandas untuk membuat Pohon Keputusan.

3.1 Mengimpor perpustakaan

#  pandas  ,        pd
import pandas as pd

3.2 Bingkai data dan Seri

Panda menggunakan struktur seperti Data frame dan Series.

Mari kita lihat tabel mirip Excel berikut.



Satu baris data disebut Seri, kolom disebut atribut data ini, dan seluruh tabel disebut bingkai Data.

3.3 Membuat bingkai Data

Kami menghubungkan spreadsheet Excel menggunakan read_excel atau ExcelWriter

#  Excel   ,     ipynb
df0 = pd.read_excel("data_golf.xlsx")
 
#  DataFrame  HTML  
from IPython.display import HTML
html = "<div style='font-family:\"メイリオ\";'>"+df0.to_html()+"</div>"
HTML(html)
 
#   Excel  (with   f.close)
with pd.ExcelWriter("data_golf2.xlsx") as f: 
       df0.to_excel(f)

Membuat Bingkai Data dari Kamus (Array Asosiatif): Kamus menyatukan data dari kolom DataFrame

#   :    
 
d = {
    "":["","","","","","","","","","","","","",""],
    "":["","","","","","","","","","","","","",""], 
    "":["","","","","","","","","","","","","",""],
    "":["","","","","","","","","","","","","",""],
 
"":["×","×","○","○","○","×","○","×","○","○","○","○","○","×"],
}
df0 = pd.DataFrame(d)

Membuat Bingkai Data dari Array: Mengumpulkan Data dari Baris DataFrame

#   :     
d = [["","","","","×"],
     ["","","","","×"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","×"],
     ["","","","","○"],
     ["","","","","×"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","○"],
     ["","","","","×"],
    ]
#        columns  index .  ,   ,    .

df0 = pd.DataFrame(d,columns=["","","","",""],index=range(len(d)))

3.4 Mendapatkan informasi dari tabel

#    
 
#    
print(df0.shape) #  (14, 5)
 
#    
print(df0.shape[0]) #  14
 
#   
print(df0.columns) #  Index(['', '', '', '', ''], dtype='object')
 
#    (  df0 -    ）
print(df0.index) #  RangeIndex(start=0, stop=14, step=1)

3.5 Mengambil nilai loc iloc

#  
 
#  ,    
#       №1 ( )
print(df0.loc[1,""]) #  

#  ,       
#        1,2,4,      Data Frame-  
df = df0.loc[[1,2,4],["",""]]
print(df)
# 
#                    
# 1                               ×
# 2                        ○
# 3                            ○
# 4                            ○

# iloc     .    0.
#      1  3,    .   iloc  ,   1:4,  4-   . 
df = df0.iloc[1:4,:-1]
print(df)
# 
#                
# 1                              
# 2                    
# 3                        


#      (Series)
#      . s  Series
s = df0.iloc[0,:]
#  ,    ,      s[" "]
print(s[""]) #  

#       (numpy.ndarray).
print(df0.values)

3.6 Perulangan melalui data, melalui data dengan iterrows iteritems

#  ,  
#     .     .
for i,row in df0.iterrows():
    # i    ( ), row  Series
    print(i,row)
    pass

#     .    .
for i,col in df0.iteritems():
    # i   , col  Series
    print(i,col)
    pass

3.7 Frekuensi value_counts

#   
#      . s  Series
s = df0.loc[:,""]

#     
print(s.value_counts())
# 
#     5
#     5
#     4
# Name: , dtype: int64

# ,   ,   “”
print(s.value_counts()[""]) #  5

3.8 Mengambil Data Permintaan Khusus

#   
#  ,   - .
print(df0.query("==''"))
# 
#                    
# 0                            ×
# 1                            ×
# 7                            ×
# 8                            ○
# 10                                ○

#  ,   - ,     
print(df0.query("=='' and =='○'"))
# 
#                    
# 8                            ○
# 10                                ○

#  ,   - ,     
print(df0.query("=='' or =='○'"))
# 
#                    
# 0                            ×
# 1                            ×
# 2                        ○
# 3                            ○
# 4                            ○
# 6                        ○
# 7                            ×
# 8                            ○
# 9                                ○
# 10                                ○
# 11                            ○
# 12                                ○

Terima kasih sudah membaca!



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