我有两个数据帧df1和df2,其中df2是df1的子集。我如何得到一个新的数据帧(df3),这是两个数据帧之间的差异?
换句话说,一个在df1中所有的行/列都不在df2中的数据帧?
我有两个数据帧df1和df2,其中df2是df1的子集。我如何得到一个新的数据帧(df3),这是两个数据帧之间的差异?
换句话说,一个在df1中所有的行/列都不在df2中的数据帧?
当前回答
对称差分
如果你只对其中一个数据帧中的行感兴趣,而不是两个数据帧中的行,你在寻找集的差异:
pd.concat([df1,df2]).drop_duplicates(keep=False)
⚠️只有在两个数据帧都不包含任何重复的情况下才有效。
设置差分/关系代数差分
如果你对关系代数差异/集差异感兴趣,即df1-df2或df1\df2:
pd.concat([df1,df2,df2]).drop_duplicates(keep=False)
⚠️只有在两个数据帧都不包含任何重复的情况下才有效。
其他回答
Pandas现在提供了一个新的API来处理数据帧差异:Pandas . datafframe .compare
df.compare(df2)
col1 col3
self other self other
0 a c NaN NaN
2 NaN NaN 3.0 4.0
import pandas as pd
# given
df1 = pd.DataFrame({'Name':['John','Mike','Smith','Wale','Marry','Tom','Menda','Bolt','Yuswa',],
'Age':[23,45,12,34,27,44,28,39,40]})
df2 = pd.DataFrame({'Name':['John','Smith','Wale','Tom','Menda','Yuswa',],
'Age':[23,12,34,44,28,40]})
# find elements in df1 that are not in df2
df_1notin2 = df1[~(df1['Name'].isin(df2['Name']) & df1['Age'].isin(df2['Age']))].reset_index(drop=True)
# output:
print('df1\n', df1)
print('df2\n', df2)
print('df_1notin2\n', df_1notin2)
# df1
# Age Name
# 0 23 John
# 1 45 Mike
# 2 12 Smith
# 3 34 Wale
# 4 27 Marry
# 5 44 Tom
# 6 28 Menda
# 7 39 Bolt
# 8 40 Yuswa
# df2
# Age Name
# 0 23 John
# 1 12 Smith
# 2 34 Wale
# 3 44 Tom
# 4 28 Menda
# 5 40 Yuswa
# df_1notin2
# Age Name
# 0 45 Mike
# 1 27 Marry
# 2 39 Bolt
另一个可能的解决方案是使用numpy广播:
df1[np.all(~np.all(df1.values == df2.values[:, None], axis=2), axis=0)]
输出:
Name Age
1 Mike 45
4 Marry 27
7 Bolt 39
nice @liangli的解决方案略有变化,不需要改变现有数据框架的索引:
newdf = df1.drop(df1.join(df2.set_index('Name').index))
除了公认的答案,我想提出一个更广泛的解决方案,可以找到两个数据框架的2D集差异与任何索引/列(他们可能不符合两个数据框架)。此外,该方法允许设置浮动元素的容忍度,用于数据帧比较(它使用np.isclose)
import numpy as np
import pandas as pd
def get_dataframe_setdiff2d(df_new: pd.DataFrame,
df_old: pd.DataFrame,
rtol=1e-03, atol=1e-05) -> pd.DataFrame:
"""Returns set difference of two pandas DataFrames"""
union_index = np.union1d(df_new.index, df_old.index)
union_columns = np.union1d(df_new.columns, df_old.columns)
new = df_new.reindex(index=union_index, columns=union_columns)
old = df_old.reindex(index=union_index, columns=union_columns)
mask_diff = ~np.isclose(new, old, rtol, atol)
df_bool = pd.DataFrame(mask_diff, union_index, union_columns)
df_diff = pd.concat([new[df_bool].stack(),
old[df_bool].stack()], axis=1)
df_diff.columns = ["New", "Old"]
return df_diff
例子:
In [1]
df1 = pd.DataFrame({'A':[2,1,2],'C':[2,1,2]})
df2 = pd.DataFrame({'A':[1,1],'B':[1,1]})
print("df1:\n", df1, "\n")
print("df2:\n", df2, "\n")
diff = get_dataframe_setdiff2d(df1, df2)
print("diff:\n", diff, "\n")
Out [1]
df1:
A C
0 2 2
1 1 1
2 2 2
df2:
A B
0 1 1
1 1 1
diff:
New Old
0 A 2.0 1.0
B NaN 1.0
C 2.0 NaN
1 B NaN 1.0
C 1.0 NaN
2 A 2.0 NaN
C 2.0 NaN