熊猫df.iterrows（）方法可访问一定数量的行：

Question

I am looping through a dataframe using df.iterrows(). Instead of looping through all the rows, I would like to set the number of rows accessed each time. First I would like to access the first two rows, then it will be the third to the sixth row and then the remaining. Is there a way to loop through the rows?

Here is what I have:

 import pandas as pd
 import numpy as np
 df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))

 df['key1'] = 0
 df.key1.iloc[0:3] = 1
 df.key1.iloc[3:7] = 2
 df.key1.iloc[7:] = 3

 df_grouped = df.groupby('key1')

 for group_name, group_value in df_grouped:
     fig, axes = plt.subplots(rows, 1, sharex=True, sharey=True, figsize= (15, 20))
     for i,r in group_value.iterrows():
         rows, columns = group_value.shape
         r = r[0:columns-1]
         r.plot(kind='bar', fill=False, log=False)  

Answer 1

You could create a column according your condition, then do a group by over that column and iterated over the grouped data.

df = pd.DataFrame(np.random.randn(10, 4), columns=list('ABCD'))

df['key1'] = 0
df.key1.iloc[1:3] = 1
df.key1.iloc[3:7] = 2
df.key1.iloc[7:] = 3

df_grouped = df.groupby('key1')

for group_name, group_value in df_grouped:
    for i,r in group_value.iterrows():
        print i, max(r[:-1])
    print '-' * 80