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
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
import pandas as pd
import io
texts = ['''\
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 ''',
'''\
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''']
df1 = pd.read_fwf(io.StringIO(texts[0]), widths=[5,7,25,21,20])
df2 = pd.read_fwf(io.StringIO(texts[1]), widths=[5,7,25,21,20])
df = pd.concat([df1,df2])
print(df)
# id Name score isEnrolled Comment
# 0 111 Jack 2.17 True He was late to class
# 1 112 Nick 1.11 False Graduated
# 2 113 Zoe 4.12 True NaN
# 0 111 Jack 2.17 True He was late to class
# 1 112 Nick 1.21 False Graduated
# 2 113 Zoe 4.12 False On vacation
df.set_index(['id', 'Name'], inplace=True)
print(df)
# score isEnrolled Comment
# id Name
# 111 Jack 2.17 True He was late to class
# 112 Nick 1.11 False Graduated
# 113 Zoe 4.12 True NaN
# 111 Jack 2.17 True He was late to class
# 112 Nick 1.21 False Graduated
# 113 Zoe 4.12 False On vacation
def report_diff(x):
return x[0] if x[0] == x[1] else '{} | {}'.format(*x)
changes = df.groupby(level=['id', 'Name']).agg(report_diff)
print(changes)
打印
score isEnrolled Comment
id Name
111 Jack 2.17 True He was late to class
112 Nick 1.11 | 1.21 False Graduated
113 Zoe 4.12 True | False nan | On vacation
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
突出显示两个数据框架之间的差异
可以使用DataFrame样式属性来突出显示有差异的单元格的背景颜色。
使用原始问题中的示例数据
第一步是用concat函数水平连接dataframe，并用keys参数区分每一帧:
df_all = pd.concat([df.set_index('id'), df2.set_index('id')],
axis='columns', keys=['First', 'Second'])
df_all
交换列级别并将相同的列名放在彼此旁边可能更容易:
df_final = df_all.swaplevel(axis='columns')[df.columns[1:]]
df_final
现在，很容易看出不同的框架。但是，我们可以进一步使用style属性来突出显示不同的单元格。我们定义了一个自定义函数来实现这一点，您可以在本部分文档中看到。
def highlight_diff(data, color='yellow'):
attr = 'background-color: {}'.format(color)
other = data.xs('First', axis='columns', level=-1)
return pd.DataFrame(np.where(data.ne(other, level=0), attr, ''),
index=data.index, columns=data.columns)
df_final.style.apply(highlight_diff, axis=None)
这将突出显示两者都有缺失值的单元格。您可以填充它们或提供额外的逻辑，这样它们就不会被突出显示。
