0

I'm trying to change the columns of a dataframe to no avail.

Here is the dataframe:

>>> file = open("data.csv", "r")
>>> data = pd.DataFrame(file)
>>> print(data)
                                                   0
0   date,1. open,2. high,3. low,4. close,5. volume\n
1  2020-01-14,316.7,317.57,312.17,312.68,40653457...
2  2020-01-15,311.85,315.5,309.55,311.34,30480882...
3  2020-01-16,313.59,315.7,312.09,315.24,27207254...

Here is the data.rename function to change the column names as documented - https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html

#Trying to change the columns with a dict
>>> data.rename(columns={"date": "date", "1. open": "open", "2. high": "high", "3. low": "low", "5. volume": "volume"})
                                                   0
0   date,1. open,2. high,3. low,4. close,5. volume\n
1  2020-01-14,316.7,317.57,312.17,312.68,40653457...
2  2020-01-15,311.85,315.5,309.55,311.34,30480882...
3  2020-01-16,313.59,315.7,312.09,315.24,27207254...

What am I doing wrong?

Updating: Thanks for all the responses.

I explicitly defined the column that I wanted to see and it all worked perfected.

>>> df.columns = ['col1', 'col2', 'col3', 'col4', 'col5', 'col6']
>>> print (df)
         col1    col2    col3    col4    col5        col6
0  2020-01-14  316.70  317.57  312.17  312.68  40653457.0
1  2020-01-15  311.85  315.50  309.55  311.34  30480882.0
2  2020-01-16  313.59  315.70  312.09  315.24  27207254.0
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2
  • Read your dataframe with pandas.read_csv. Currently you are producing a single column with the name 0. Commented Jun 6, 2020 at 16:01
  • what does data = pd.read_csv() give you? Commented Jun 6, 2020 at 16:27

3 Answers 3

Reset to default
1

As others have said, use pd.read_csv("data.csv", columns=['col1', 'col2',...]) when reading a csv to a DataFrame.

Also, here's an easy way to change DataFrame column names:

df.columns = ['col_name1', 'col_name2', ...]
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3 Comments

Thank you very much, bug. New tool in my arsenal!
When I use this within the interpreter, it worked perfectly. But when I use it within data.py package by explicitly defining the columns, the print is still invoking the old attributes. df = data df.column = ['date', 'open', 'high', 'low', 'close', 'volume'] print(df) And I still got this: df.column = ['date', 'open', 'high', 'low', 'close', 'volume'] 1. open 2. high 3. low 4. close 5. volume date 2020-01-14 163.3900 163.6000 161.7200 162.13 23500783.0
Found my typo in df.columns. Thank you again, bug.
0

You don't need to open the file when using pandas;

data = pandas.read_csv('path_to/your_file.csv')

I always add inplace=True to the rename function.

But if you just want to remove the numbers from the column names, you could as well do this;

data.columns = [col.split()[1] for col in data.columns]
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1 Comment

Thank you, Sy. This one is very much for the keep. The only problem with my columns is that one of them has only index [0], so the split iterate doesn't work. I could use a 'While True' statement to check if there is a split applicable to each column.
0

you can explicitly specify the new column names(Apart from the ones which are already there in the file) while reading the file as follows:

df = pd.read_csv('data.csv', columns=['new_col1', 'new_col2', ...])

But if you won't add the columns attribute then bydefault the column names mentioned in the csv file will be taken by the dataframe.

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1 Comment

This is perfect! I didn't realize that I could explicitly define how I want the columns. This solves the problem beautifully! Thank you very much, Vaibhav.

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