pandas中两个数据帧之间的差异

Question

我有两个数据帧，它们都有相同的基本模式。(4个日期字段、几个字符串字段和4-5个浮点型字段)。叫它们df1和df2。

我想要做的基本上是得到两个数据帧的“不同”--在这里我得到所有没有在两个数据帧之间共享的行(不是在集合交集中)。请注意，这两个数据帧的长度不必相同。

我尝试使用pandas.merge(how='outer')，但我不确定要传递哪个列作为“键”，因为确实没有键，而且我尝试的各种组合都不起作用。df1或df2可能有两行(或更多行)相同。

在pandas/Python中做这件事的好方法是什么？

Answer 1

试试这个：

diff_df = pd.merge(df1, df2, how='outer', indicator='Exist')

diff_df = diff_df.loc[diff_df['Exist'] != 'both']

您将拥有一个包含所有在df1和df2上都不存在的行的数据帧。

Answer 2

IIUC：

您可以使用pd.Index.symmetric_difference

pd.concat([df1, df2]).loc[
    df1.index.symmetric_difference(df2.index)
]

Answer 3

您可以使用此函数，输出是6个数据帧的有序字典，您可以将其写入excel进行进一步分析。

唯一键‘df1’和'df2‘是指您的输入键，而唯一键是指组成唯一键的列或列的组合。(即'Fruits')

• 'dedupe‘(default=True)删除df1和df2中的重复项。(参见comments)

• 'labels‘中的步骤4(默认值= ('df1'，’df2‘)允许您命名输入数据帧。如果一个唯一键存在于两个数据帧中，但在一个或多个列中具有不同的值，通常重要的是知道这些行，将它们放在另一个行的顶部，并用名称标记该行，这样我们就可以知道它属于哪个数据帧。

• ‘drop’可以在考虑difference

时获取要排除在考虑范围之外的列的列表

如下所示：

df1 = pd.DataFrame([['apple', '1'], ['banana', 2], ['coconut',3]], columns=['Fruits','Quantity'])
df2 = pd.DataFrame([['apple', '1'], ['banana', 3], ['durian',4]], columns=['Fruits','Quantity'])
dict1 = diff_func(df1, df2, 'Fruits')

In [10]: dict1['df1_only']:
Out[10]:
    Fruits Quantity
1  coconut        3

In [11]: dict1['df2_only']:
Out[11]:
   Fruits Quantity
3  durian        4

In [12]: dict1['Diff']:
Out[12]:
   Fruits Quantity df1 or df2
0  banana        2        df1
1  banana        3        df2

In [13]: dict1['Merge']:
Out[13]:
  Fruits Quantity
0  apple        1

代码如下：

import pandas as pd
from collections import OrderedDict as od

def diff_func(df1, df2, uid, dedupe=True, labels=('df1', 'df2'), drop=[]):
    dict_df = {labels[0]: df1, labels[1]: df2}
    col1 = df1.columns.values.tolist()
    col2 = df2.columns.values.tolist()

    # There could be columns known to be different, hence allow user to pass this as a list to be dropped.
    if drop:
        print ('Ignoring columns {} in comparison.'.format(', '.join(drop)))
        col1 = list(filter(lambda x: x not in drop, col1))
        col2 = list(filter(lambda x: x not in drop, col2))
        df1 = df1[col1]
        df2 = df2[col2]


    # Step 1 - Check if no. of columns are the same:
    len_lr = len(col1), len(col2)
    assert len_lr[0]==len_lr[1], \
    'Cannot compare frames with different number of columns: {}.'.format(len_lr)

    # Step 2a - Check if the set of column headers are the same
    #           (order doesnt matter)
    assert set(col1)==set(col2), \
    'Left column headers are different from right column headers.' \
       +'\n   Left orphans: {}'.format(list(set(col1)-set(col2))) \
       +'\n   Right orphans: {}'.format(list(set(col2)-set(col1)))

    # Step 2b - Check if the column headers are in the same order
    if col1 != col2:
        print ('[Note] Reordering right Dataframe...')
        df2 = df2[col1]

    # Step 3 - Check datatype are the same [Order is important]
    if set((df1.dtypes == df2.dtypes).tolist()) - {True}:
        print ('dtypes are not the same.')
        df_dtypes = pd.DataFrame({labels[0]:df1.dtypes,labels[1]:df2.dtypes,'Diff':(df1.dtypes == df2.dtypes)})
        df_dtypes = df_dtypes[df_dtypes['Diff']==False][[labels[0],labels[1],'Diff']]
        print (df_dtypes)
    else:
        print ('DataType check: Passed')

    # Step 4 - Check for duplicate rows
    if dedupe:
        for key, df in dict_df.items():
            if df.shape[0] != df.drop_duplicates().shape[0]:
                print(key + ': Duplicates exists, they will be dropped.')
                dict_df[key] = df.drop_duplicates()

    # Step 5 - Check for duplicate uids.
    if type(uid)==str or type(uid)==list:
        print ('Uniqueness check: {}'.format(uid))
        for key, df in dict_df.items():
            count_uid = df.shape[0]
            count_uid_unique = df[uid].drop_duplicates().shape[0]
            var = [0,1][count_uid_unique == df.shape[0]] #<-- Round off to the nearest integer if it is 100%
            pct = round(100*count_uid_unique/df.shape[0], var)
            print ('{}: {} out of {} are unique ({}%).'.format(key, count_uid_unique, count_uid, pct))

    # Checks complete, begin merge. '''Remenber to dedupe, provide labels for common_no_match'''
    dict_result = od()
    df_merge = pd.merge(df1, df2, on=col1, how='inner')
    if not df_merge.shape[0]:
        print ('Error: Merged DataFrame is empty.')
    else:
        dict_result[labels[0]] = df1
        dict_result[labels[1]] = df2
        dict_result['Merge'] = df_merge
        if type(uid)==str:
            uid = [uid]

        if type(uid)==list:
            df1_only = df1.append(df_merge).reset_index(drop=True)
            df1_only['Duplicated']=df1_only.duplicated(keep=False)  #keep=False, marks all duplicates as True
            df1_only = df1_only[df1_only['Duplicated']==False]
            df2_only = df2.append(df_merge).reset_index(drop=True)
            df2_only['Duplicated']=df2_only.duplicated(keep=False)
            df2_only = df2_only[df2_only['Duplicated']==False]

            label = labels[0]+' or '+labels[1]
            df_lc = df1_only.copy()
            df_lc[label] = labels[0]
            df_rc = df2_only.copy()
            df_rc[label] = labels[1]
            df_c = df_lc.append(df_rc).reset_index(drop=True)
            df_c['Duplicated'] = df_c.duplicated(subset=uid, keep=False)
            df_c1 = df_c[df_c['Duplicated']==True]
            df_c1 = df_c1.drop('Duplicated', axis=1)
            df_uc = df_c[df_c['Duplicated']==False]

            df_uc_left = df_uc[df_uc[label]==labels[0]]
            df_uc_right = df_uc[df_uc[label]==labels[1]]

            dict_result[labels[0]+'_only'] = df_uc_left.drop(['Duplicated', label], axis=1)
            dict_result[labels[1]+'_only'] = df_uc_right.drop(['Duplicated', label], axis=1)
            dict_result['Diff'] = df_c1.sort_values(uid).reset_index(drop=True)

    return dict_result