Basic question on a pandas dataframe. I have a 1x1 dataframe with a datapoint and there are no column headers (currently). df[0,0]
does not work because I think it is expecting a column name. In addition, df.0
doesn't work nor df[0,'']
. df.ix[0,0]
does work.
In general, am I required to have a column name? Is it a best practice to use column names with pandas dataframes? If my sql query does not have column headers, is it best to add them at that point?
Thanks for the help.
The most common way to remove a column is using df. drop() . Sometimes, del command in Python is also used.
The solution can be improved as data. rename( columns={0 :'new column name'}, inplace=True ) . There is no need to use 'Unnamed: 0' , simply use the column number, which is 0 in this case and then supply the 'new column name' . With Pandas 1.0.
You can get column names in Pandas dataframe using df. columns statement. Usecase: This is useful when you want to show all columns in a dataframe in the output console (E.g. in the jupyter notebook console).
pandas to CSV without Header To write DataFrame to CSV without column header (remove column names) use header=False param on to_csv() method.
Nope, you're not required to assign column names, nor do you need them to access any element.
In [12]: df = pd.DataFrame([0])
In [13]: df.ix[0,0]
Out[13]: 0
In [14]: df[0][0]
Out[14]: 0
In fact, you can think of the column already having a name -- it is the integer 0. Look at what happens when you provide a name
In [15]: df #Before naming the column
Out[15]:
0
0 0
In [16]: df.columns = ['ColA']
In [17]: df #Renamed
Out[17]:
ColA
0 0
In [18]: df['ColA'][0] #Now you can access the column using the new name
Out[18]: 0
In [19]: df[0][0] #... but trying the old name will not work
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
KeyError: 'no item named 0'
You can still use DataFrame.ix
just as before, though:
In [20]: df.ix[0,0]
Out[20]: 0
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