新增新列

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

print(df)
# Output: 
#    A  B
# 0  1  4
# 1  2  5
# 2  3  6

直接分配

df['C'] = [7, 8, 9]

print(df)
# Output: 
#    A  B  C
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9

新增常量列

df['C'] = 1

print(df)

# Output: 
#    A  B  C
# 0  1  4  1
# 1  2  5  1
# 2  3  6  1

列作為其他列中的表示式

df['C'] = df['A'] + df['B']

# print(df)
# Output: 
#    A  B  C
# 0  1  4  5
# 1  2  5  7
# 2  3  6  9

df['C'] = df['A']**df['B']

print(df)
# Output:
#    A  B    C
# 0  1  4    1
# 1  2  5   32
# 2  3  6  729

操作是按元件計算的，因此如果我們將列作為列表

a = [1, 2, 3]
b = [4, 5, 6]

最後一個表示式中的列將獲得為

c = [x**y for (x,y) in zip(a,b)]

print(c)
# Output:
# [1, 32, 729]

動態建立它

df_means = df.assign(D=[10, 20, 30]).mean()

print(df_means)
# Output: 
# A     2.0
# B     5.0
# C     7.0
# D    20.0  # adds a new column D before taking the mean
# dtype: float64

新增多個列

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df[['A2','B2']] = np.square(df)

print(df)
# Output:
#    A  B  A2  B2
# 0  1  4   1  16
# 1  2  5   4  25
# 2  3  6   9  36

動態新增多個列

new_df = df.assign(A3=df.A*df.A2, B3=5*df.B)

print(new_df)
# Output:
#    A  B  A2  B2  A3  B3
# 0  1  4   1  16   1  20
# 1  2  5   4  25   8  25
# 2  3  6   9  36  27  30