如何将具有单个 csv 列的 Pandas 数据框插入 MySQL 数据库

Question

I have a pandas dataframe that I read from google sheet. I then added the tag column using:

df['tag'] = df.filter(like = 'Subject', axis = 1).apply(lambda x: np.where(x == 'Y', x.name,'')).values.tolist()
df['tag'] = df['tag'].apply(lambda x: [i for i in x if i!= ''])

Resultant sample DataFrame:

    Id  Name    Subject-A   Subject-B   Total   tag
0   1   A       Y                       100     [Subject-A]
1   2   B                   Y           98      [Subject-B]
2   3   C       Y           Y           191     [Subject-A, Subject-B]
3   4   D                   Y           100     [Subject-B]
4   5   E                   Y           95      [Subject-B]

Then I export the dataframe to a MySQL Database after converting the tag column into a comma separated string by:

df['tag'] = df['tag'].map(lambda x : ', '.join(str(i) for i in x)).str.replace('Subject-','')
df

    Id  Name    Subject-A   Subject-B   Total   tag
0   1   A       Y                       100     A
1   2   B                   Y           98      B
2   3   C       Y           Y           91      A, B
3   4   D                   Y           100     B
4   5   E                   Y           95      B

df.to_sql(name = 'table_name', con = conn, if_exists = 'replace', index = False)

But in the MySQL database the tag columns is:

A,
,B
A,B
,B
,B

My actual data has many such "Subject" columns so the result looks like:

, , , D
A, ,C,
...
...

Could someone please let me know why it's giving expected out in Pandas but when I save the dataframe in cloud SQL, the column looks different. The expected output in MySQL database is same as how the tag column is appearing in Pandas.

Answer 1

Here is alternative solution, seems some data related problem.

First filter Subject columns with remove Subject- and then use DataFrame.dot with columns names with separator, last strip separator from right side:

df1 = df.filter(like = 'Subject').rename(columns=lambda x: x.replace('Subject-',''))
print (df1)
     A    B
0    Y  NaN
1  NaN    Y
2    Y    Y
3  NaN    Y
4  NaN    Y

df['tag'] = df1.eq('Y').dot(df1.columns  + ', ').str.rstrip(', ')
print (df)
   Id Name Subject-A Subject-B  Total   tag
0   1    A         Y       NaN    100     A
1   2    B       NaN         Y     98     B
2   3    C         Y         Y    191  A, B
3   4    D       NaN         Y    100     B
4   5    E       NaN         Y     95     B