Question

我之前在这里已经找到了这个问题的答案，但是似乎没有一个对我有用。现在，我有一个带有客户列表及其地址的数据框。但是，每个地址都分为许多列，我正在尝试将它们全部放在一列之下。

到目前为止，我阅读的代码如下：

data1_df['Address'] = data1_df['Address 1'].map(str) + ", " + data1_df['Address 2'].map(str) + ", " +  data1_df['Address 3'].map(str) + ", " + data1_df['city'].map(str) + ", " +  data1_df['city'].map(str) + ", " +  data1_df['Province/State'].map(str) + ", " +  data1_df['Country'].map(str) + ", " +  data1_df['Postal Code'].map(str)

但是，我得到的错误是： TypeError：一元加号期望使用数字dtype，而不是对象

我不确定为什么它不按原样接受字符串，而是使用+运算符。加号不应该容纳物体吗？

Answer 1

希望此示例对您有帮助：

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1,2,3],
                   'B': list('ABC'),
                   'C': [4,5,np.nan],
                   'D': ['One', np.nan, 'Three']})

addColumns = ['B', 'C', 'D']

df['Address'] = df[addColumns].astype(str).apply(lambda x: ', '.join([i for i in x if i != 'nan']), axis=1)

df

#   A  B    C      D      Address
#0  1  A  4.0    One  A, 4.0, One
#1  2  B  5.0    NaN       B, 5.0
#2  3  C  NaN  Three     C, Three

以上内容将以str的{{1}}表示为NaN的形式出现。

或者您可以用空字符串填充nan：

NaN

Answer 2

对于需要将NaN值加在一起的列，请遵循以下逻辑：

def add_cols_w_nan(df, col_list, space_char, new_col_name):
    """ Add together multiple columns where some of the columns 
    may contain NaN, with the appropriate amount of spacing between columns. 

    Examples:
        'Mr.' + NaN + 'Smith' becomes 'Mr. Smith'
        'Mrs.' + 'J.' + 'Smith' becomes 'Mrs. J. Smith'
        NaN + 'J.' + 'Smith' becomes 'J. Smith'

    Args:
        df: pd.DataFrame
            DataFrame for which strings are added together.
        col_list: ORDERED list of column names, eg. ['first_name', 
            'middle_name', 'last_name']. The columns will be added in order. 
        space_char: str
            Character to insert between concatenation of columns.
        new_col_name: str
            Name of the new column after adding together strings.

    Returns: pd.DataFrame with a string addition column

    """
    df2 = df[col_list].copy()

    # Convert to strings, leave nulls alone
    df2 = df2.where(df2.isnull(), df2.astype('str'))

    # Add space character, NaN remains NaN, which is important
    df2.loc[:, col_list[1:]] = space_char + df2.loc[:, col_list[1:]]

    # Fix rows where leading columns are null
    to_fix = df2.notnull().idxmax(1)
    for col in col_list[1:]:
        m = to_fix == col
        df2.loc[m, col] = df2.loc[m, col].str.replace(space_char, '')

    # So that summation works
    df2[col_list] = df2[col_list].replace(np.NaN, '')

    # Add together all columns
    df[new_col_name] = df2[col_list].sum(axis=1)
    # If all are missing replace with missing
    df[new_col_name] = df[new_col_name].replace('', np.NaN)

    del df2
    return df

样本数据：

import pandas as pd
import numpy as np
df = pd.DataFrame({'Address 1': ['AAA', 'ABC', np.NaN, np.NaN, np.NaN],
                   'Address 2': ['foo', 'bar', 'baz', None, np.NaN],
                   'Address 3': [np.NaN, np.NaN, 17, np.NaN, np.NaN],
                   'city': [np.NaN, 'here', 'there', 'anywhere', np.NaN],
                   'state': ['NY', 'TX', 'WA', 'MI', np.NaN]})

#  Address 1 Address 2  Address 3      city state
#0       AAA       foo        NaN       NaN    NY
#1       ABC       bar        NaN      here    TX
#2       NaN       baz       17.0     there    WA
#3       NaN      None        NaN  anywhere    MI
#4       NaN       NaN        NaN       NaN   NaN

df = add_cols_w_nan(
    df,
    col_list = ['Address 1', 'Address 2', 'Address 3', 'city', 'state'],
    space_char = ', ',
    new_col_name = 'full_address')

df.full_address.tolist()
#['AAA, foo, NY', 
# 'ABC, bar, here, TX', 
# 'baz, 17.0, there, WA', 
# 'anywhere, MI',
# nan]

熊猫，串联列的值。

2 个答案:

样本数据：