Python – 转换数据帧和切片

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