如果两个数据帧中有相同的id，那么找出发生了什么变化实际上是相当容易的。只要执行frame1 != frame2，就会得到一个布尔型的DataFrame，其中每个True都是已更改的数据。由此，您可以通过执行changedids = frame1.index[np。Any (frame1 != frame2,axis=1)]。

第一部分类似于Constantine，你可以得到哪个行是空的布尔值*:

In [21]: ne = (df1 != df2).any(1)

In [22]: ne
Out[22]:
0    False
1     True
2     True
dtype: bool

然后我们可以看到哪些条目发生了变化:

In [23]: ne_stacked = (df1 != df2).stack()

In [24]: changed = ne_stacked[ne_stacked]

In [25]: changed.index.names = ['id', 'col']

In [26]: changed
Out[26]:
id  col
1   score         True
2   isEnrolled    True
    Comment       True
dtype: bool

这里的第一个条目是索引，第二个条目是已更改的列。

In [27]: difference_locations = np.where(df1 != df2)

In [28]: changed_from = df1.values[difference_locations]

In [29]: changed_to = df2.values[difference_locations]

In [30]: pd.DataFrame({'from': changed_from, 'to': changed_to}, index=changed.index)
Out[30]:
               from           to
id col
1  score       1.11         1.21
2  isEnrolled  True        False
   Comment     None  On vacation

*注意:重要的是df1和df2在这里共享相同的索引。为了克服这种模糊性，可以使用df1确保只查看共享标签。Index & df2。索引，但我还是把它留作练习吧。

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

我遇到过这个问题，但在找到这篇文章之前找到了答案:

根据unutbu的回答，加载你的数据…

import pandas as pd
import io

texts = ['''\
id   Name   score                    isEnrolled                       Date
111  Jack                            True              2013-05-01 12:00:00
112  Nick   1.11                     False             2013-05-12 15:05:23
     Zoe    4.12                     True                                  ''',

         '''\
id   Name   score                    isEnrolled                       Date
111  Jack   2.17                     True              2013-05-01 12:00:00
112  Nick   1.21                     False                                
     Zoe    4.12                     False             2013-05-01 12:00:00''']


df1 = pd.read_fwf(io.StringIO(texts[0]), widths=[5,7,25,17,20], parse_dates=[4])
df2 = pd.read_fwf(io.StringIO(texts[1]), widths=[5,7,25,17,20], parse_dates=[4])

定义你的diff函数…

def report_diff(x):
    return x[0] if x[0] == x[1] else '{} | {}'.format(*x)

然后你可以简单地使用Panel来总结:

my_panel = pd.Panel(dict(df1=df1,df2=df2))
print my_panel.apply(report_diff, axis=0)

#          id  Name        score    isEnrolled                       Date
#0        111  Jack   nan | 2.17          True        2013-05-01 12:00:00
#1        112  Nick  1.11 | 1.21         False  2013-05-12 15:05:23 | NaT
#2  nan | nan   Zoe         4.12  True | False  NaT | 2013-05-01 12:00:00

顺便说一下，如果你在IPython Notebook中，你可能喜欢使用彩色差异函数 根据单元格是否不同、相等或左/右为空来给出颜色:

from IPython.display import HTML
pd.options.display.max_colwidth = 500  # You need this, otherwise pandas
#                          will limit your HTML strings to 50 characters

def report_diff(x):
    if x[0]==x[1]:
        return unicode(x[0].__str__())
    elif pd.isnull(x[0]) and pd.isnull(x[1]):
        return u'<table style="background-color:#00ff00;font-weight:bold;">'+\
            '<tr><td>%s</td></tr><tr><td>%s</td></tr></table>' % ('nan', 'nan')
    elif pd.isnull(x[0]) and ~pd.isnull(x[1]):
        return u'<table style="background-color:#ffff00;font-weight:bold;">'+\
            '<tr><td>%s</td></tr><tr><td>%s</td></tr></table>' % ('nan', x[1])
    elif ~pd.isnull(x[0]) and pd.isnull(x[1]):
        return u'<table style="background-color:#0000ff;font-weight:bold;">'+\
            '<tr><td>%s</td></tr><tr><td>%s</td></tr></table>' % (x[0],'nan')
    else:
        return u'<table style="background-color:#ff0000;font-weight:bold;">'+\
            '<tr><td>%s</td></tr><tr><td>%s</td></tr></table>' % (x[0], x[1])

HTML(my_panel.apply(report_diff, axis=0).to_html(escape=False))

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

这个答案只是扩展了@Andy Hayden的答案，使其能够适应数值字段为nan的情况，并将其包装成一个函数。

import pandas as pd
import numpy as np


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):
        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 = ['id', 'col']
        difference_locations = np.where(diff_mask)
        changed_from = df1.values[difference_locations]
        changed_to = df2.values[difference_locations]
        return pd.DataFrame({'from': changed_from, 'to': changed_to},
                            index=changed.index)

所以对于你的数据(稍微编辑一下，在分数列中有一个NaN):

import sys
if sys.version_info[0] < 3:
    from StringIO import StringIO
else:
    from io import StringIO

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')
diff_pd(df1, df2)

输出:

                from           to
id  col                          
112 score       1.11         1.21
113 isEnrolled  True        False
    Comment           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

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:

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。

在摆弄了@journois的答案后，由于Panel的弃用，我能够使用MultiIndex而不是Panel来工作。

首先，创建一些虚拟数据:

df1 = pd.DataFrame({
    'id': ['111', '222', '333', '444', '555'],
    'let': ['a', 'b', 'c', 'd', 'e'],
    'num': ['1', '2', '3', '4', '5']
})
df2 = pd.DataFrame({
    'id': ['111', '222', '333', '444', '666'],
    'let': ['a', 'b', 'c', 'D', 'f'],
    'num': ['1', '2', 'Three', '4', '6'],
})

然后，定义你的diff函数，在这种情况下，我将使用他的答案report_diff保持不变:

def report_diff(x):
    return x[0] if x[0] == x[1] else '{} | {}'.format(*x)

然后，我将数据连接到一个MultiIndex数据框架:

df_all = pd.concat(
    [df1.set_index('id'), df2.set_index('id')], 
    axis='columns', 
    keys=['df1', 'df2'],
    join='outer'
)
df_all = df_all.swaplevel(axis='columns')[df1.columns[1:]]

最后，我将对每个列组应用report_diff:

df_final.groupby(level=0, axis=1).apply(lambda frame: frame.apply(report_diff, axis=1))

这个输出:

         let        num
111        a          1
222        b          2
333        c  3 | Three
444    d | D          4
555  e | nan    5 | nan
666  nan | f    nan | 6

这就是全部!

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

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。

如果您发现这个线程试图在测试中比较数据名称，那么请查看assert_frame_equal方法:https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.testing.assert_frame_equal.html