我试图突出显示两个数据帧之间发生了什么变化。
假设我有两个Python Pandas数据框架:
"StudentRoster Jan-1":
id Name score isEnrolled Comment
111 Jack 2.17 True He was late to class
112 Nick 1.11 False Graduated
113 Zoe 4.12 True
"StudentRoster Jan-2":
id Name score isEnrolled Comment
111 Jack 2.17 True He was late to class
112 Nick 1.21 False Graduated
113 Zoe 4.12 False On vacation
我的目标是输出一个HTML表，它:
标识已更改的行(可以是int, float, boolean，字符串)
输出具有相同的OLD和NEW值的行(理想情况下是HTML表)，以便消费者可以清楚地看到两个数据框架之间发生了什么变化:
“StudentRoster差异Jan-1 - Jan-2”:
id名称分数isregistered评论
尼克是1.11|现在1.21假毕业
113佐伊4.12是真的|现在是假的|现在“度假”
我想我可以逐行逐列比较，但有没有更简单的方法?
在两个数据帧之间寻找不对称差异的函数实现如下:
(基于熊猫的集差)
要点:https://gist.github.com/oneryalcin/68cf25f536a25e65f0b3c84f9c118e03
def diff_df(df1, df2, how="left"):
"""
Find Difference of rows for given two dataframes
this function is not symmetric, means
diff(x, y) != diff(y, x)
however
diff(x, y, how='left') == diff(y, x, how='right')
Ref: https://stackoverflow.com/questions/18180763/set-difference-for-pandas/40209800#40209800
"""
if (df1.columns != df2.columns).any():
raise ValueError("Two dataframe columns must match")
if df1.equals(df2):
return None
elif how == 'right':
return pd.concat([df2, df1, df1]).drop_duplicates(keep=False)
elif how == 'left':
return pd.concat([df1, df2, df2]).drop_duplicates(keep=False)
else:
raise ValueError('how parameter supports only "left" or "right keywords"')
例子:
df1 = pd.DataFrame(d1)
Out[1]:
Comment Name isEnrolled score
0 He was late to class Jack True 2.17
1 Graduated Nick False 1.11
2 Zoe True 4.12
df2 = pd.DataFrame(d2)
Out[2]:
Comment Name isEnrolled score
0 He was late to class Jack True 2.17
1 On vacation Zoe True 4.12
diff_df(df1, df2)
Out[3]:
Comment Name isEnrolled score
1 Graduated Nick False 1.11
2 Zoe True 4.12
diff_df(df2, df1)
Out[4]:
Comment Name isEnrolled score
1 On vacation Zoe True 4.12
# This gives the same result as above
diff_df(df1, df2, how='right')
Out[22]:
Comment Name isEnrolled score
1 On vacation Zoe True 4.12
使用concat和drop_duplicate的不同方法:
import sys
if sys.version_info[0] < 3:
from StringIO import StringIO
else:
from io import StringIO
import pandas as pd
DF1 = StringIO("""id Name score isEnrolled Comment
111 Jack 2.17 True "He was late to class"
112 Nick 1.11 False "Graduated"
113 Zoe NaN True " "
""")
DF2 = StringIO("""id Name score isEnrolled Comment
111 Jack 2.17 True "He was late to class"
112 Nick 1.21 False "Graduated"
113 Zoe NaN False "On vacation" """)
df1 = pd.read_table(DF1, sep='\s+', index_col='id')
df2 = pd.read_table(DF2, sep='\s+', index_col='id')
#%%
dictionary = {1:df1,2:df2}
df=pd.concat(dictionary)
df.drop_duplicates(keep=False)
输出:
Name score isEnrolled Comment
id
1 112 Nick 1.11 False Graduated
113 Zoe NaN True
2 112 Nick 1.21 False Graduated
113 Zoe NaN False On vacation
在两个数据帧之间寻找不对称差异的函数实现如下:
(基于熊猫的集差)
要点:https://gist.github.com/oneryalcin/68cf25f536a25e65f0b3c84f9c118e03
def diff_df(df1, df2, how="left"):
"""
Find Difference of rows for given two dataframes
this function is not symmetric, means
diff(x, y) != diff(y, x)
however
diff(x, y, how='left') == diff(y, x, how='right')
Ref: https://stackoverflow.com/questions/18180763/set-difference-for-pandas/40209800#40209800
"""
if (df1.columns != df2.columns).any():
raise ValueError("Two dataframe columns must match")
if df1.equals(df2):
return None
elif how == 'right':
return pd.concat([df2, df1, df1]).drop_duplicates(keep=False)
elif how == 'left':
return pd.concat([df1, df2, df2]).drop_duplicates(keep=False)
else:
raise ValueError('how parameter supports only "left" or "right keywords"')
例子:
df1 = pd.DataFrame(d1)
Out[1]:
Comment Name isEnrolled score
0 He was late to class Jack True 2.17
1 Graduated Nick False 1.11
2 Zoe True 4.12
df2 = pd.DataFrame(d2)
Out[2]:
Comment Name isEnrolled score
0 He was late to class Jack True 2.17
1 On vacation Zoe True 4.12
diff_df(df1, df2)
Out[3]:
Comment Name isEnrolled score
1 Graduated Nick False 1.11
2 Zoe True 4.12
diff_df(df2, df1)
Out[4]:
Comment Name isEnrolled score
1 On vacation Zoe True 4.12
# This gives the same result as above
diff_df(df1, df2, how='right')
Out[22]:
Comment Name isEnrolled score
1 On vacation Zoe True 4.12
扩展@cge的答案，这对于结果的可读性来说非常酷:
a[a != b][np.any(a != b, axis=1)].join(pd.DataFrame('a<->b', index=a.index, columns=['a<=>b'])).join(
b[a != b][np.any(a != b, axis=1)]
,rsuffix='_b', how='outer'
).fillna('')
完整的演示示例:
import numpy as np, pandas as pd
a = pd.DataFrame(np.random.randn(7,3), columns=list('ABC'))
b = a.copy()
b.iloc[0,2] = np.nan
b.iloc[1,0] = 7
b.iloc[3,1] = 77
b.iloc[4,2] = 777
a[a != b][np.any(a != b, axis=1)].join(pd.DataFrame('a<->b', index=a.index, columns=['a<=>b'])).join(
b[a != b][np.any(a != b, axis=1)]
,rsuffix='_b', how='outer'
).fillna('')
结果:样本
在线演示
import pandas as pd
import numpy as np
df = pd.read_excel('D:\\HARISH\\DATA SCIENCE\\1 MY Training\\SAMPLE DATA & projs\\CRICKET DATA\\IPL PLAYER LIST\\IPL PLAYER LIST _ harish.xlsx')
df1= srh = df[df['TEAM'].str.contains("SRH")]
df2 = csk = df[df['TEAM'].str.contains("CSK")]
srh = srh.iloc[:,0:2]
csk = csk.iloc[:,0:2]
csk = csk.reset_index(drop=True)
csk
srh = srh.reset_index(drop=True)
srh
new = pd.concat([srh, csk], axis=1)
new.head()
**
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