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?
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()
**
玩家类型
0 David Warner Batsman…多尼女士，机长
1 Bhuvaneshwar Kumar Bowler…拉文德拉·加德贾是全才
Manish Pandey Batsman…苏雷什·莱纳全能
拉希德·汗·阿尔曼·鲍勒…基达尔·贾达夫全能
4 Shikhar Dhawan Batsman ....多面手Dwayne Bravo
pandas >= 1.1: DataFrame.compare
使用pandas 1.1，基本上可以用一个函数调用复制Ted Petrou的输出。例子摘自文档:
pd.__version__
# '1.1.0'
df1.compare(df2)
score isEnrolled Comment
self other self other self other
1 1.11 1.21 NaN NaN NaN NaN
2 NaN NaN 1.0 0.0 NaN On vacation
这里，“self”指的是LHS数据帧，而“other”指的是RHS数据帧。默认情况下，相等的值将被nan替换，因此您可以只关注差异。如果您想显示相同的值，请使用
df1.compare(df2, keep_equal=True, keep_shape=True)
score isEnrolled Comment
self other self other self other
1 1.11 1.21 False False Graduated Graduated
2 4.12 4.12 True False NaN On vacation
你也可以使用align_axis改变比较轴:
df1.compare(df2, align_axis='index')
score isEnrolled Comment
1 self 1.11 NaN NaN
other 1.21 NaN NaN
2 self NaN 1.0 NaN
other NaN 0.0 On vacation
这是逐行比较值，而不是逐列比较值。
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
下面是另一种使用选择和合并的方法:
In [6]: # first lets create some dummy dataframes with some column(s) different
...: df1 = pd.DataFrame({'a': range(-5,0), 'b': range(10,15), 'c': range(20,25)})
...: df2 = pd.DataFrame({'a': range(-5,0), 'b': range(10,15), 'c': [20] + list(range(101,105))})
In [7]: df1
Out[7]:
a b c
0 -5 10 20
1 -4 11 21
2 -3 12 22
3 -2 13 23
4 -1 14 24
In [8]: df2
Out[8]:
a b c
0 -5 10 20
1 -4 11 101
2 -3 12 102
3 -2 13 103
4 -1 14 104
In [10]: # make condition over the columns you want to comapre
...: condition = df1['c'] != df2['c']
...:
...: # select rows from each dataframe where the condition holds
...: diff1 = df1[condition]
...: diff2 = df2[condition]
In [11]: # merge the selected rows (dataframes) with some suffixes (optional)
...: diff1.merge(diff2, on=['a','b'], suffixes=('_before', '_after'))
Out[11]:
a b c_before c_after
0 -4 11 21 101
1 -3 12 22 102
2 -2 13 23 103
3 -1 14 24 104
以下是来自Jupyter的截图:
