Pandas

用於數據處理和分析。它提供了兩種主要的數據結構，即DataFrame（類似於表格）和Series（類似於一維數組）。Pandas允許您輕鬆地讀取、處理和分析各種數據，包括CSV文件、Excel文件、SQL數據庫等。

使用字典或陣列建立DataFrame

import pandas as pd
grades = {
    "name": ["David", "Melvin", "Romain", "Ricky"],
    "math": [80, 75, 77, 86],
    "chinese": [65, 90, 85, 70]
}
 
df = pd.DataFrame(grades)
 
print("使用字典來建立df：")
print(df)
 
print("=====================")
 
grades = [
    ["David", 80, 65],
    ["Melvin", 75, 90],
    ["Romain", 77, 85],
    ["Ricky", 86, 70]
]
 
new_df = pd.DataFrame(grades)
 
print("使用陣列來建立df：")
print(new_df)

選擇DataFrame的部分資料

import pandas as pd
 
grades = {
    "name": ["David", "Melvin", "Romain", "Ricky"],
    "math": [80, 75, 93, 86],
    "chinese": [63, 90, 85, 70]
}
 
df = pd.DataFrame(grades)
print("原來的df")
print(df)
 
print("=================================")
 
new_df = df.head(2)
print("取得最前面的兩筆資料")
print(new_df)

使用Series

import pandas as pd

cars = ["BMW", "BENZ", "Toyota", "Nissan", "Lexus"]

select = pd.Series(cars)
print(select)

使用帶有索引的Series

import pandas as pd

dict = {  
    "factory": "Taichung",
    "sensor1": "1",
    "sensor2": "2",
    "sensor3": "3",
    "sensor4": "4",
    "sensor5": "5"
}

select = pd.Series(dict, index = dict.keys()) # 排序與原 dict 相同  
print(select[0])  
print("=====")  
print(select['sensor1'])  
print("=====")  
print(select[[0, 2, 4]])  
print("=====")  
print(select[['factory', 'sensor1', 'sensor3']])

自訂DataFrame的索引和欄位名稱

import pandas as pd
 
 
grades = {
    "name": ["Mike", "Sherry", "Cindy", "John"],
    "math": [80, 75, 93, 86],
    "chinese": [63, 90, 85, 70]
}
 
df = pd.DataFrame(grades)
df.index = ["s1", "s2", "s3", "s4"]  #自訂索引值
df.columns = ["student_name", "math_score", "chinese_score"]  #自訂欄位名稱
print(df)