我有一个小的测试数据样本:
import pandas as pd
df = {'ID': ['H900','H901','H902','','M1435','M149','M157','','M699','M920','','M789','M617','M991','H903','M730','M191'],
'Clone': [0,1,2,2,2,2,2,2,3,3,3,4,4,4,5,5,6],
'Length': [48,42 ,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48]}
df = pd.DataFrame(df)
看起来像:
df
Out[4]:
Clone ID Length
0 0 H900 48
1 1 H901 42
2 2 H902 48
3 2 48
4 2 M1435 48
5 2 M149 48
6 2 M157 48
7 2 48
8 3 M699 48
9 3 M920 48
10 3 48
11 4 M789 48
12 4 M617 48
13 4 M991 48
14 5 H903 48
15 5 M730 48
16 6 M191 48
我想要一个简单的脚本,例如随机选择5行,但仅选择包含ID的行,它不应包含不包含ID的任何行。
我的脚本:
import pandas as pd
import numpy as np
df = {'ID': ['H900','H901','H902','','M1435','M149','M157','','M699','M920','','M789','M617','M991','H903','M730','M191'],
'Clone': [0,1,2,2,2,2,2,2,3,3,3,4,4,4,5,5,6],
'Length': [48,42 ,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48]}
df = pd.DataFrame(df)
rows = np.random.choice(df.index.values, 5)
sampled_df = df.ix[rows]
sampled_df.to_csv('sampled_df.txt', sep = '\t', index=False)
但是此脚本有时会选择不包含ID的行
我认为您需要ID
使用boolean indexing
以下内容清空过滤器:
import pandas as pd
import numpy as np
df = {'ID': ['H900','H901','H902','','M1435','M149','M157','','M699','M920','','M789','M617','M991','H903','M730','M191'],
'Clone': [0,1,2,2,2,2,2,2,3,3,3,4,4,4,5,5,6],
'Length': [48,42 ,48,48,48,48,48,48,48,48,48,48,48,48,48,48,48]}
df = pd.DataFrame(df)
print (df)
df = df[df.ID != '']
rows = np.random.choice(df.index.values, 5)
sampled_df = df.loc[rows]
print (sampled_df)
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