当我调用将两个DataFrame合并在一起时，我总是得到与我试图进行内部合并的DataFrame相同的输出。我让这个函数为另一个做同样事情的函数工作，但是具有不同的两个 Dataframe 的集合。（我处理健康数据，因此我尝试自动编辑并合并这些文件，以使我的新用户（ef_in）与系统中当前的所有用户相对应（ul_in），以及从在ul_in上但不在ef_in上的用户禁用它们）。

def client_merge(ef_in, ul_in):
    # ef_in = pd. read_csv("COC_Ready_EF.csv", sep=',')
    # ul_in = pd.read_csv("Ready UserList.csv", sep=',')
    pd.set_option('mode.chained_assignment', None)

    ef_in['UniqueID'] = ef_in['UniqueID'].astype(object)
    ef_in['ZipCode'] = ef_in['ZipCode'].astype(object)
    ef_in['HireDate'] = ef_in['HireDate'].astype(object)
    ef_in['DateOfBirth'] = ef_in['DateOfBirth'].astype(object)
    ul_in['UniqueID'] = ul_in['UniqueID'].astype(object)
    ul_in['Action'] = ul_in['Action'].astype(object)
    ul_in['ZipCode'] = ul_in['ZipCode'].astype(object)

    df = pd.concat(([ef_in, ul_in]), axis=0, ignore_index=True, sort=False)
    df.drop_duplicates(subset=['UniqueID'], keep=False, inplace=True)

    new_users = df.merge(ef_in)
    disable_users = df.merge(ul_in)

    disable_users['Action'].fillna('Disable', inplace=True)
    ready_to_print_file = pd.concat([new_users, disable_users], ignore_index=False)

    rtpf1 = ready_to_print_file[ready_to_print_file["FirstName"].str.contains("Admin") == False]
    rtpf2 = rtpf1[rtpf1["FirstName"].str.contains("Clarks") == False]
    rtpf3 = rtpf2[rtpf2["FirstName"].str.contains("Test") == False]

    rtpf3.to_csv(path, header=True, index=False)

我从这得到ul_in：

def client_ul_formatter(in_file):
    pd.set_option('mode.chained_assignment', None)
    in_file = pd.read_csv(in_file, sep=',')

    df = in_file[['FirstName', 'LastName', 'Region', 'UniqueID', 'DateOfBirth', 'Gender',
                  'ZipCode', 'Email', 'Role', 'HireDate', 'Company', 'Action']]

    return df

我的名字来自：

def rotate_date(strg, n):
    return strg[n:] + strg[:n]


def client_ef_formatter(input_file):
    pd.set_option('mode.chained_assignment', None)
    input_file = pd.read_csv(input_file, sep=',',
                             dtype={'HIREDATE': str, 'DATE OF BIRTH': str})

    df = input_file[['LAST NAME', 'FIRST AND MIDDLE', 'DATE OF BIRTH', 'GENDER', 'RELATIONSHIP',
                     'HIREDATE', 'ZIP', 'ALT ID', 'EMAIL ADDRESS']]

    df = df.drop(df[df['RELATIONSHIP'] != 'E'].index)

    df = df.drop(['RELATIONSHIP'], axis=1)

    p = -1
    hdf = []
    for _ in (df['HIREDATE']):
        p = (p + len(df['HIREDATE']) - (len(df['HIREDATE']) - 1))
        hd = df['HIREDATE'].iloc[p]
        f = rotate_date(hd, -4)
        hdf.append(f)

    q = -1
    ddf = []
    for _ in (df['DATE OF BIRTH']):
        q = (q + len(df['DATE OF BIRTH']) - (len(df['DATE OF BIRTH']) - 1))
        dob = df['DATE OF BIRTH'].iloc[q]
        f = rotate_date(dob, -4)
        ddf.append(f)

    df['HIREDATE'] = hdf

    df['DATE OF BIRTH'] = ddf

    df['DATE OF BIRTH'] = pd.to_datetime(df['DATE OF BIRTH'], errors='coerce', format='%m%d%Y')

    df['HIREDATE'] = pd.to_datetime(df['HIREDATE'], errors='coerce', format='%m%d%Y')

    df.rename(columns={'HIREDATE': 'HireDate', 'LAST NAME': 'LastName', 'FIRST AND MIDDLE': 'FirstName',
                       'DATE OF BIRTH': 'DateOfBirth', 'ALT ID': 'UniqueID', 'GENDER': 'Gender',
                       'ZIP': 'ZipCode', 'EMAIL ADDRESS': 'Email'}, inplace=True)

    df['Region'] = pd.Series(dtype=str)
    df['Role'] = pd.Series(dtype=str)
    df['Company'] = pd.Series(dtype=str)
    df['Action'] = pd.Series(dtype=str)

    df = df.reindex(columns=['FirstName', 'LastName', 'Region', 'UniqueID', 'DateOfBirth', 'Gender', 'ZipCode', 'Email',
                             'Role', 'HireDate', 'Company', 'Action'])

    df['Company'].fillna('client_account', inplace=True)
    df['Role'].fillna('Employee On Plan', inplace=True)

    df.to_csv(path, header=True, index=False)

这让我停了下来。我很不确定，因为它不是在抛出错误。