peaceSTSW peaceSTSW - 1 year ago 256
MySQL Question

python pandas dataframe to_sql converting an object to Mysql INT datatype yields incorrect results

I am trying to read a csv file into a Pandas dataframe and insert the final dataframe into Mysql using pandas.to_sql function.

All the columns are inserting the correct data except for one column in dataframe which has a length of 25 characters. This column (transaction_id) is defined as a INT(25) in MYSQL and I have not been able to figure out why this column has wrong data.

And the weird thing is, the transaction_id column in MySQL has the same value for more than 360K rows per csv file.

Any help would be great.

Client transaction ID example:
format: transaction id_page id-banner id
2343213254646775357496618_12-586542237 \n
2343213254646775357881218_14-586542237 \n
2343213254646775357886268_10-586542237 \n
2343213254646775357886218_27-586542237 \n
2343213254646775357886248_10-586542237 \n

Here is my code:

xls = pd.ExcelFile(path_value)
df = xls.parse('report', skiprows=13, index_col=None, na_values=['NA'])

# remove last row
df = df[:-1]
df['transaction_datetime'] = pd.to_datetime(df['transaction_datetime'])

# add transaction date column to data frame:
df['transaction_date'] = df['transaction_datetime']
df.loc[:, 'created_date'] =

# convert client transaction id into three parts
df['transaction_id'], df['placeholder'] = zip(
*df['Client Transaction ID'].apply(lambda x: x.split('_', 1)))
df['page_id'], df['banner_id'] = zip(*df['placeholder'].apply(lambda x: x.split('-', 1)))

df.drop('placeholder', axis=1, inplace=True)
df.drop('Client Transaction ID', axis=1, inplace=True)

# connect to mysql
engine = create_engine(
'connection string'
df.to_sql(name='table', con=engine, if_exists='append', index=False)

Answer Source

If i understood your code correctly you can parse transaction_id,page_id,banner_id using vectorized .str.extract() method and doing it "in one shot":

In [32]: df
    Client Transaction ID
0  tx0001_page01-banner01
1  tx0002_page01-banner23
2  tx0003_page33-banner56
3  tx0004_page12-banner76
4  tx0005_page44-banner11

In [33]: df[['transaction_id','page_id','banner_id']] = \
    ...:     df.pop('Client Transaction ID').str.extract(r'^([^_]*)_([^-]*)-(.*)',
    ...:                                                 expand=True)

In [34]: df
  transaction_id page_id banner_id
0         tx0001  page01  banner01
1         tx0002  page01  banner23
2         tx0003  page33  banner56
3         tx0004  page12  banner76
4         tx0005  page44  banner11

PS you didn't provide any sample data so i had to reconstruct it by your code...

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