概述
我附上了截图以帮助解释.我有一个从克利夫兰心脏数据集中提取的数据框,该数据集占用76列并将它们放入7列,并将其他列包装到下一行.我试图弄清楚如何将该数据帧变为可读格式,如右侧的数据框所示.
变量xyz将始终相同,但我列出的其他字母变量将不同.我以为我可以使用data.loc [：,：’xyz’]开始,但我不知道从哪里开始：
data = pd.read_csv("../resources/cleveland.data")
data.loc[:,:'xyz']
然后我必须从那里开始为这些变量分配列名.令人惊讶的是,一旦我解决了这个问题,火车,测试,验证部分将更加容易.在此先感谢您的帮助. (我是菜鸟)
1 a b c
d xyz 2 e
f g h xyz
3 i j k
码
import pandas as pd
import numpy as np
# The initial data doesn't contain header so set header to None
df = pd.read_csv("../resources/cleveland.data",header=None)
cols = df.columns.tolist()
# Reset the index to get the line number in the durty file
df = df.reset_index()
# After having melt the df,you can filter the df in order to have every values in one column.
# Those values are in the right order
df = pd.melt(df,id_vars=['index'],value_vars=cols)
df = df.sort_values(by=['index','variable'])
# Then you can set the line number
df['line'] = np.where(df.value == 'xyz',1,np.nan)
df.line = df.line.cumsum()
df.line = df.line.bfill()
# If the file doesn't end with 'xyz',we have to set the line number to df.line.max() + 1
df.loc[df.line.isna(),'line'] = df.line.max() + 1
df.line = df.line.ffill()
# We can set the column names as interger with a groupby cumsum
df['one'] = 1
df['col_name'] = df.groupby(['line'])['one'].cumsum()
df['col_name'] = "col_" + df['col_name'].astype('str')
# Then we can pivot the table
df = df[['value','line','col_name']]
df = df.pivot(index='line',columns='col_name',values='value')
print(df)
输出数据
col_name col_1 col_2 col_3 col_4 col_5 col_6
line
1.0 1 a b c d xyz
2.0 2 e f g h xyz
3.0 3 i j k NaN NaN
总结
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