[solved] dataframe and read_csv

[ju21878436312]

Aim: I would like to convert data, read with read_csv and convert it to a dataframe.
What I've tried: 1. data = pd.read_csv(...) 2. pd.DataFrame(data)
Problem: The columns are not shown in the dataframe as expected in 2 columns.

import pandas as pd import numpy as np from datetime import datetime, timedelta # read data, date_parser=[0]: first column to datetime, data = pd.read_csv('minimal_data.csv', delimiter = ';', date_parser=[0], usecols=[0, 1], header = 0, names = ["MyColumn1","MyColumn2"]), print(data) df = pd.DataFrame(data) print(df) 

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MacOS 10.15.7, Jupyter notebook

[Larz60+]

Hello,

I.m not an expert with pandas, however:

• data is already a dataframe.
  
• drop lines 10 - 13 and all will be fine.
  

import pandas as pd import numpy as np from datetime import datetime, timedelta # read data, date_parser=[0]: first column to datetime, data = pd.read_csv('minimal_data.csv', delimiter = ';', date_parser=[0], usecols=[0, 1], header = 0, names = ["MyColumn1","MyColumn2"]), print(data) 

Output:
( MyColumn1 MyColumn2 0 09.06.2021 14:35:05 100 1 09.06.2021 14:36:16 100 2 09.06.2021 14:37:26 100 3 09.06.2021 14:38:37 100 4 09.06.2021 14:39:48 100 5 09.06.2021 14:40:59 100 6 09.06.2021 14:42:10 100 7 09.06.2021 14:43:21 100 8 09.06.2021 14:44:32 100,)

[ju21878436312]

Thank you. I actually need to further evaluate the data. And if I try it directly with data, I get:

data.dtypes AttributeError: 'tuple' object has no attribute 'dtypes'

or

data.loc[data['MyColumn2'] == 0] AttributeError: 'tuple' object has no attribute 'iloc'

[snippsat]

hank you. I actually need to further evaluate the data. And if I try it directly with data, I get:

You need to get thee DataFrame out of tuple.
Here a example with some advice.

import pandas as pd import numpy as np # Pandas has own datateime do not need to use this #from datetime import datetime, timedelta # read data, date_parser=[0]: first column to datetime, data = pd.read_csv('minimal_data.csv', delimiter = ';', date_parser=[0], usecols=[0, 1], header=0, names=["MyColumn1","MyColumn2"]), # Get DataFrame out of tupe df = data[0] # Convert to datetime64 df['MyColumn1'] = pd.to_datetime(df['MyColumn1']) print(df.dtypes) print(df) print('-' * 30) print(df.loc[df['MyColumn2'] == 0])

Output:
MyColumn1 datetime64[ns] MyColumn2 int64 dtype: object MyColumn1 MyColumn2 0 2021-09-06 14:35:05 178 1 2021-09-06 14:36:16 59 2 2021-09-06 14:37:26 0 3 2021-09-06 14:38:37 0 4 2021-09-06 14:39:48 0 5 2021-09-06 14:40:59 0 6 2021-09-06 14:42:10 0 7 2021-09-06 14:43:21 0 8 2021-09-06 14:44:32 0 ------------------------------ MyColumn1 MyColumn2 2 2021-09-06 14:37:26 0 3 2021-09-06 14:38:37 0 4 2021-09-06 14:39:48 0 5 2021-09-06 14:40:59 0 6 2021-09-06 14:42:10 0 7 2021-09-06 14:43:21 0 8 2021-09-06 14:44:32 0

[ju21878436312]

@snippsat: Thank you very much for the useful comments!