我有两个CSV文件,我正在比较并仅并排返回具有不同值的列。
df1
Country 1980 1981 1982 1983 1984
Bermuda 0.00793 0.00687 0.00727 0.00971 0.00752
Canada 9.6947 9.58952 9.20637 9.18989 9.78546
Greenland 0.00791 0.00746 0.00722 0.00505 0.00799
Mexico 3.72819 4.11969 4.33477 4.06414 4.18464
df2
Country 1980 1981 1982 1983 1984
Bermuda 0.77777 0.00687 0.00727 0.00971 0.00752
Canada 9.6947 9.58952 9.20637 9.18989 9.78546
Greenland 0.00791 0.00746 0.00722 0.00505 0.00799
Mexico 3.72819 4.11969 4.33477 4.06414 4.18464
import pandas as pd
import numpy as np
df1=pd.read_csv('csv1.csv')
df2=pd.read_csv('csv2.csv')
def diff_pd(df1, df2):
"""Identify differences between two pandas DataFrames"""
assert (df1.columns == df2.columns).all(), \
"DataFrame column names are different"
if any(df1.dtypes != df2.dtypes):
"Data Types are different, trying to convert"
df2 = df2.astype(df1.dtypes)
if df1.equals(df2):
print("Dataframes are the same")
return None
else:
# need to account for np.nan != np.nan returning True
diff_mask = (df1 != df2) & ~(df1.isnull() & df2.isnull())
ne_stacked = diff_mask.stack()
changed = ne_stacked[ne_stacked]
changed.index.names = ['Country', 'Column']
difference_locations = np.where(diff_mask)
changed_from = df1.values[difference_locations][0]
changed_to = df2.values[difference_locations]
y=pd.DataFrame({'From': changed_from, 'To': changed_to},
index=changed.index)
print(y)
return pd.DataFrame({'From': changed_from, 'To': changed_to},
index=changed.index)
diff_pd(df1,df2)
我当前的输出是:
From To
Country Column
0 1980 0.00793 0.77777
因此,我想获取值不匹配的行的国家/地区名称,而不是索引0。下面是一个示例。
我希望我的输出是:
From To
Country Column
Bermuda 1980 0.00793 0.77777
感谢所有可以提供解决方案的人。
答案 0 :(得分:0)
一种更短的方法,其重命名过程如下:
def process_df(df):
res = df.set_index('Country').stack()
res.index.rename('Column', level=1, inplace=True)
return res
df1 = process_df(df1)
df2 = process_df(df2)
mask = (df1 != df2) & ~(df1.isnull() & df2.isnull())
df3 = pd.concat([df1[mask], df2[mask]], axis=1).rename({0:'From', 1:'To'}, axis=1)
df3
From To
Country Column
Bermuda 1980 0.00793 0.77777