Remove Unnamed columns in pandas dataframe

Remove Unnamed columns in pandas dataframe

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I have a data file from columns A-G like below but when I am reading it with pd.read_csv('data.csv') it prints an extra unnamed column at the end for no reason.

colA    ColB    colC    colD    colE    colF    colG    Unnamed: 7
44      45      26      26      40      26      46        NaN
47      16      38      47      48      22      37        NaN
19      28      36      18      40      18      46        NaN
50      14      12      33      12      44      23        NaN
39      47      16      42      33      48      38        NaN

I have seen my data file various times but I have no extra data in any other column. How I should remove this extra column while reading ? Thanks


df = df.loc[:, ~df.columns.str.contains('^Unnamed')]

In [162]: df
Out[162]:
   colA  ColB  colC  colD  colE  colF  colG
0    44    45    26    26    40    26    46
1    47    16    38    47    48    22    37
2    19    28    36    18    40    18    46
3    50    14    12    33    12    44    23
4    39    47    16    42    33    48    38

if the first column in the CSV file has index values, then you can do this instead:

df = pd.read_csv('data.csv', index_col=0)

Remove Unnamed columns in pandas dataframe, How do you remove an index from a Dataframe in Python? How to get rid of Unnamed: column in a pandas dataframe. I have a situation wherein sometimes when I read a csv from df I get an unwanted index-like column named unnamed:0. This is very annoying! I have tried


First, find the columns that have 'unnamed', then drop those columns. Note: You should Add inplace = True to the .drop parameters as well.

df.drop(df.columns[df.columns.str.contains('unnamed',case = False)],axis = 1, inplace = True)

Rename unnamed column pandas dataframe, df = df.loc[:, ~df.columns.str.contains('^Unnamed')] In [162]: df Out[162]: colA ColB colC colD colE colF colG 0 44 45 26 26 40 26 46 1 47 16 38  One way of renaming the columns in a Pandas dataframe is by using the rename() function. This method is quite useful when we need to rename some selected columns because we need to specify information only for the columns which are to be renamed. Rename a single column.


The pandas.DataFrame.dropna function removes missing values (e.g. NaN, NaT).

For example the following code would remove any columns from your dataframe, where all of the elements of that column are missing.

df.dropna(how='all', axis='columns')

How to drop the index column of a pandas DataFrame in Python, We first import this file using the pd.read_csv command from the Pandas library. Area Frequency Unnamed: 2 Unnamed: 3 0 Accounting 73.0 NaN NaN 1 Finance 52.0 NaN Drop column 3 and 4 and let's have a look at the data again. Drop a column in python In pandas, drop() function is used to remove column(s). axis=1 tells Python that you want to apply function on columns instead of rows. df.drop(['A'], axis=1) Column A has been removed.


The approved solution doesn't work in my case, so my solution is the following one:

    ''' The column name in the example case is "Unnamed: 7"
 but it works with any other name ("Unnamed: 0" for example). '''

        df.rename({"Unnamed: 7":"a"}, axis="columns", inplace=True)

        # Then, drop the column as usual.

        df.drop(["a"], axis=1, inplace=True)

Hope it helps others.

How to Get the Column Names from a Pandas Dataframe, import pandas as pd. df=pd.read_csv('dfairannual.csv'). After importing the dataset, we get an unnamed column as follows: Now to remove such  Function for removing all null columns from the data frame: def Remove_Null_Columns(df): dff = pd.DataFrame() for cl in fbinst: if df[cl].isnull().sum() == len(df[cl]): pass else: dff[cl] = df[cl] return dff. This function will remove all Null columns from the df. share.


Unnamed: 0, Private, Apps, Accept, Enroll, Top10perc, Top25perc, F.Undergrad, P.Undergrad, Outstate, Room.Board, Books, Personal, PhD, Terminal, S.F.Ratio  We can Remove or Delete a specified column or sprcified columns by drop() method. Suppose df is a dataframe. Column to be removed = column0. Code: df = df.drop(column0, axis=1) To remove multiple columns col1, col2, . . . , coln, we have to insert all the columns that needed to be removed in a list. Then remove them by drop() method. Code:


DataFrame(np.random.randn(5,3), columns=list('abc')) pd.read_csv(io.StringIO(df​.to_csv())) Out[37]: Unnamed: 0 a b c 0 0 0.109066 -1.112704  Let’s discuss how to drop one or multiple columns in Pandas Dataframe. Drop one or more than one columns from a DataFrame can be achieved in multiple ways. Create a


To remove the unnamed columns we can use two different methods; loc and drop​, together with other Pandas dataframe methods. When using  Add an Index, Row, or Column. To assign the ‘index’ argument to the input, ensure that you get the selected index. If nothing is specified in the data frame, by default, it will have a numerically valued index beginning from 0. You can make your index by calling set_index() on your data frame and re-use them.