[英]Append new Pandas DataFrame to an old one without column names sorted
我將新的數據框附加到舊的數據框:
import numpy as np
import pandas as pd
from pandas import Series
from pandas import DataFrame
df1 = DataFrame(np.arange(3.).reshape((1, 3)), columns=list('dcb'), index=['Ohio'])
df2 = DataFrame(np.arange(3.).reshape((1, 3)), columns=list('bdc'), index=['Utah'])
print df1
print df2
print pd.concat([df1, df2])
然后我得到這樣的結果:
d c b
Ohio 0.0 1.0 2.0
b d c
Utah 0.0 1.0 2.0
b c d
Ohio 2.0 1.0 0.0
Utah 0.0 2.0 1.0
但是我希望結果中的列不按“ bcd”排序,而是按原點“ dcb”排序:
d c b
Ohio 0.0 1.0 2.0
Utah 1.0 2.0 0.0
使用join_axes
參數:
pd.concat([df1, df2], join_axes=[df1.columns])
您可以將原始訂單存儲在變量中,然后在合並后重新應用它:
df1 = DataFrame(np.arange(3.).reshape((1, 3)), columns=list('dcb'), index=['Ohio'])
orig_column_order = df1.columns
df2 = DataFrame(np.arange(3.).reshape((1, 3)), columns=list('bdc'), index=['Utah'])
combined = pd.concat([df1, df2], keys=list('dbc'))
combined = combined[orig_column_order]
print(df1)
print(df2)
print(combined)
給出:
d c b
Ohio 0.0 1.0 2.0
b d c
Utah 0.0 1.0 2.0
d c b
d Ohio 0.0 1.0 2.0
b Utah 1.0 2.0 0.0
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