import pandas as pd
# 創(chuàng)建一個(gè)Series
s = pd.Series([1, 2, 3, 4, 5])
# 輸出：
# 0    1
# 1    2
# 2    3
# 3    4
# 4    5
# dtype: int64
print(s)

import pandas as pd
# 創(chuàng)建一個(gè)帶有自定義索引和值的Series
index = ['C', 'S', 'D', 'N', 'P']
s = pd.Series([1, 2, 3, 4, 5], index = index)
# 輸出：
# C    1
# S    2
# D    3
# N    4
# P    5
# dtype: int64
print(s)

import pandas as pd
# 創(chuàng)建一個(gè)帶有自定義索引和值的Series
s = pd.Series({'C': 1, 'S': 2, 'D': 3, 'N': 4, 'P': 5})
# 輸出：
# C    1
# S    2
# D    3
# N    4
# P    5
# dtype: int64
print(s)

import pandas as pd
s = pd.Series([1, 2, 3, 4, 5])
# 訪問(wèn)第二個(gè)元素，輸出：3
print(s[2])
s = pd.Series({'C': 1, 'S': 2, 'D': 3, 'N': 4, 'P': 5})
# 訪問(wèn)Key值為'D'的元素，輸出：3
print(s['D'])

import pandas as pd
s1 = pd.Series({'Red': 1, 'Blue': 2, 'Green': 3})
s2 = pd.Series({'Red': 100, 'Blue': 200, 'Green': 300})
s = s1 + s2
# 將兩個(gè)Series進(jìn)行相加，輸出：
# Red      101
# Blue     202
# Green    303
# dtype: int64
print(s)
s1 = pd.Series({'Red': 1, 'Blue': 2, 'Green': 3, 'White': 4})
s2 = pd.Series({'Red': 100, 'Blue': 200, 'Green': 300})
s = s1 + s2
# 數(shù)據(jù)標(biāo)簽不相同的數(shù)據(jù)，運(yùn)算后結(jié)果是NaN，輸出：
# Blue     202.0
# Green    303.0
# Red      101.0
# White      NaN
# dtype: float64
print(s)
# 數(shù)據(jù)標(biāo)簽不相同的數(shù)據(jù)，調(diào)用add函數(shù)，可以設(shè)置默認(rèn)填充值，輸出：
# Blue     202.0
# Green    303.0
# Red      101.0
# White      4.0
# dtype: float64
s = s1.add(s2, fill_value = 0)
print(s)

import pandas as pd
# 創(chuàng)建DataFrame
data = {'Name': ['Jack', 'Tank', 'John'], 'Age': [20, 21, 19]}
df = pd.DataFrame(data)
# 輸出：
#    Name  Age
# 0  Jack   20
# 1  Tank   21
# 2  John   19
print(df)

import pandas as pd
# 創(chuàng)建DataFrame
data = {'Name': ['Jack', 'Tank', 'John'], 'Age': [20, 21, 19]}
df = pd.DataFrame(data)
# 輸出：
#    Name  Age
# 0  Jack   20
# 1  Tank   21
# 2  John   19
print(df)
df = pd.DataFrame(data, index = ['First', 'Second', 'Third'])
# 指定自定義索引，輸出：
#         Name  Age
# First   Jack   20
# Second  Tank   21
# Third   John   19
print(df)
# 訪問(wèn)列數(shù)據(jù)，輸出：
# First     Jack
# Second    Tank
# Third     John
# Name: Name, dtype: object
print(df['Name'])
# 根據(jù)行索引訪問(wèn)行數(shù)據(jù)，輸出：
# Name    John
# Age       19
# Name: Third, dtype: object
print(df.iloc[2])
# 根據(jù)行標(biāo)簽訪問(wèn)行數(shù)據(jù)，輸出：
# Name    John
# Age       19
# Name: Third, dtype: object
print(df.loc['Third'])
df['Age'] = [22, 18, 20]
# 修改列數(shù)據(jù)，輸出：
#         Name  Age
# First   Jack   22
# Second  Tank   18
# Third   John   20
print(df)
df['Gender'] = ['M', 'F', 'F']
# 新增列數(shù)據(jù)，輸出：
#         Name  Age Gender
# First   Jack   22      M
# Second  Tank   18      F
# Third   John   20      F
print(df)
del df['Gender']
# 刪除列數(shù)據(jù)，輸出：
#         Name  Age
# First   Jack   22
# Second  Tank   18
# Third   John   20
print(df)
# 篩選出年齡大于20的數(shù)據(jù)，輸出：
#        Name  Age
# First  Jack   22
print(df[df['Age'] > 20])

import pandas as pd
# 創(chuàng)建DataFrame
data = {'Name': ['Jack', 'Tank', 'John'], 'Age': [20, 21, 19]}
df = pd.DataFrame(data)
# 將DataFrame寫(xiě)入CSV文件
df.to_csv('output.csv', index = False)

import pandas as pd
# 從CSV文件讀取 
df = pd.read_csv('output.csv')
# 輸出：
#    Name  Age
# 0  Jack   20
# 1  Tank   21
# 2  John   19
print(df)