迭代

n = 5
df = pd.DataFrame({'A':pd.date_range(start = '2021-01-01',periods = n, freq = 'D'),'x':np.linspace(0, stop = n-1,num = n),'c':np.random.choice(['low','medium','high'],n).tolist()})
print(df)

# 输出结果:
#             A    x       c
#  0 2021-01-01  0.0    high
#  1 2021-01-02  1.0  medium
#  2 2021-01-03  2.0  medium
#  3 2021-01-04  3.0    high
#  4 2021-01-05  4.0  medium

for col in df:
    print(col)

# 输出结果:
#  A
#  x
#  c

df_1 = pd.DataFrame(np.random.randn(3,3), columns=['col1','col2','col3'])
for key,item in df.iteritems()[:1]:
    print(key,'\n',item)
    print()

# 输出结果:
#  A 
#   0   2021-01-01
#  1   2021-01-02
#  2   2021-01-03
#  3   2021-01-04
#  4   2021-01-05
#  Name: A, dtype: datetime64[ns]

#  x 
#   0    0.0
#  1    1.0
#  2    2.0
#  3    3.0
#  4    4.0
#  Name: x, dtype: float64

#  c 
#   0      high
#  1    medium
#  2    medium
#  3      high
#  4    medium
#  Name: c, dtype: object

for row_index,row in df.iterrows():
    print(row_index, row)
    print()

# 输出结果:
#  0 A    2021-01-01 00:00:00
#  x                      0
#  c                   high
#  Name: 0, dtype: object

#  1 A    2021-01-02 00:00:00
#  x                      1
#  c                 medium
#  Name: 1, dtype: object

#  2 A    2021-01-03 00:00:00
#  x                      2
#  c                 medium
#  Name: 2, dtype: object

#  3 A    2021-01-04 00:00:00
#  x                      3
#  c                   high
#  Name: 3, dtype: object

#  4 A    2021-01-05 00:00:00
#  x                      4
#  c                 medium
#  Name: 4, dtype: object

for row in df.itertuples():
    print(row)
    print()

# 输出结果:
#  Pandas(Index=0, A=Timestamp('2021-01-01 00:00:00'), x=0.0, c='high')

#  Pandas(Index=1, A=Timestamp('2021-01-02 00:00:00'), x=1.0, c='medium')

#  Pandas(Index=2, A=Timestamp('2021-01-03 00:00:00'), x=2.0, c='medium')

#  Pandas(Index=3, A=Timestamp('2021-01-04 00:00:00'), x=3.0, c='high')

#  Pandas(Index=4, A=Timestamp('2021-01-05 00:00:00'), x=4.0, c='medium')