Specify dtype option on import or set low_memory=False

Question

I am using the following code:

df = pd.read_csv('/Python Test/AcquirerRussell3000.csv')

I have the following type of data:

18.07.2000  27.1875         0 08.08.2000  25.3125       0.1 05.09.2000  \
0   19.07.00  26.6250 -0.020690   09.08.00  25.2344 -0.003085   06.09.00   
1   20.07.00  26.6250  0.000000   10.08.00  25.1406 -0.003717   07.09.00   
2   21.07.00  25.6875 -0.035211   11.08.00  25.5781  0.017402   08.09.00   
3   24.07.00  26.2500  0.021898   14.08.00  25.4375 -0.005497   11.09.00   
4   25.07.00  26.6875  0.016667   15.08.00  25.5625  0.004914   12.09.00 

I am getting the following error:

Pythone Test/untitled0.py:1: DtypeWarning: Columns (long list of numbers) have mixed types. 
Specify dtype option on import or set low_memory=False.

So every 3rd column is a date the rest are numbers. I guess there is no single dtype since dates are strings and the rest is a float or int? I have about 5000 columns or more and around 400 rows.

I have seen similar questions to this but dont quite know how to apply this to my data. Furthermore I want to run the following code after to stack the data frame.

a = np.arange(len(df.columns))
df.columns = [a % 3, a // 3]
df = df.stack().reset_index(drop=True)

df.to_csv('AcquirerRussell3000stacked.csv', sep=',')

What dtype should I use? Or should I just set low_memory to false?

Answer 1

df = pd.read_csv('/Python Test/AcquirerRussell3000.csv', engine='python')

or

df = pd.read_csv('/Python Test/AcquirerRussell3000.csv', low_memory=False)

does the trick for me.