Python - 转换数据帧和切片

Question

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)

Answer 1

输入数据

1   a   b   c
d   xyz 2   e
f   g   h   xyz
3   i   j   k

代码

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