我有下面的代码片段,效果很好。
import pandas as pd
import numpy as np
prefixes = ['sj00', 'sj12', 'cr00', 'cr08', 'eu00', 'eu50']
df = pd.read_csv('new_hosts', index_col=False, header=None)
df['prefix'] = df[0].str[:4]
df['grp'] = df.groupby('prefix').cumcount()
df = df.pivot(index='grp', columns='prefix', values=0)
df['sj12'] = df['sj12'].str.extract('(\w{2}\d{2}\w\*)', expand=True)
df = df[ prefixes ].dropna(axis=0, how='all').replace(np.nan, '', regex=True)
df = df.rename_axis(None)
sj000001
sj000002
sj000003
sj000004
sj124000
sj125000
sj126000
sj127000
sj128000
sj129000
sj130000
sj131000
sj132000
cr000011
cr000012
cr000013
cr000014
crn00001
crn00002
crn00003
crn00004
euk000011
eu0000012
eu0000013
eu0000014
eu5000011
eu5000013
eu5000014
eu5000015
sj00 sj12 cr00 cr08 eu00 eu50
sj000001 cr000011 crn00001 euk000011 eu5000011
sj000002 cr000012 crn00002 eu0000012 eu5000013
sj000003 cr000013 crn00003 eu0000013 eu5000014
sj000004 cr000014 crn00004 eu0000014 eu5000015
由于代码工作正常,但正如您所看到的
current output
,第二列没有任何值但仍然出现所以,如果特定列没有任何值,我怎么可能有校验和,然后从显示中删除它?
我们可以在处理之前检查
prefixes
是否存在于数据框中以避免错误吗?
IIUC,之前
df = df[ prefixes ].dropna(axis=0, how='all').replace(np.nan, '', regex=True)
你可以这样做:
# remove all empty columns
df = df.dropna(axis=1, how='all')
这将解决你的第一部分。第二部分可以是
reindex
?
# select prefixes:
prefixes = ['sj00', 'sj12', 'cr00', 'cr08', 'eu00', 'eu50', 'sh00', 'dt00', 'sh00', 'dt00']
df = df.reindex(prefixes, axis=1).dropna(axis=1, how='all').replace(np.nan, '', regex=True)
注意
axis=1
,而不是 axis=0
与我对问题 1 的建议相同。
非常感谢 Quang Hoang 在帖子中的提示,只是为了解决方法,我按如下方式工作,直到得到更好的答案:
# Select prefixes
prefixes = ['sj00', 'sj12', 'cr00', 'cr08', 'eu00', 'eu50']
df = pd.read_csv('new_hosts', index_col=False, header=None)
df['prefix'] = df[0].str[:4]
df['grp'] = df.groupby('prefix').cumcount()
df = df.pivot(index='grp', columns='prefix', values=0)
df = df[prefixes]
# For column `sj12` only extract the values having `sj12` and a should be a word immediately after that like `sj12[a-z]`
df['sj12'] = df['sj12'].str.extract('(\w{2}\d{2}\w\*)', expand=True)
df.replace('', np.nan, inplace=True)
# Remove the empty columns
df = df.dropna(axis=1, how='all')
# again drop if all values in the row are nan and replace nan to empty for live columns
df = df.dropna(axis=0, how='all').replace(np.nan, '', regex=True)
# drop the index field
df = df.rename_axis(None)
print(df)