如何摆脱从 CSV 文件中读取的 pandas DataFrame 中的“未命名：0”列？

Question

I have a situation wherein sometimes when I read a csv from df I get an unwanted index-like column named unnamed:0 .

file.csv

,A,B,C
0,1,2,3
1,4,5,6
2,7,8,9

The CSV is read with this:

pd.read_csv('file.csv')

   Unnamed: 0  A  B  C
0           0  1  2  3
1           1  4  5  6
2           2  7  8  9

This is very annoying? Does anyone have an idea on how to get rid of this?

Answer 1

It's the index column, pass pd.to_csv(..., index=False) to not write out an unnamed index column in the first place, see the to_csv() docs .

Example:

In [37]:
df = pd.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 -0.545209
1           1  0.447114  1.525341  0.317252
2           2  0.507495  0.137863  0.886283
3           3  1.452867  1.888363  1.168101
4           4  0.901371 -0.704805  0.088335

compare with:

In [38]:
pd.read_csv(io.StringIO(df.to_csv(index=False)))

Out[38]:
          a         b         c
0  0.109066 -1.112704 -0.545209
1  0.447114  1.525341  0.317252
2  0.507495  0.137863  0.886283
3  1.452867  1.888363  1.168101
4  0.901371 -0.704805  0.088335

You could also optionally tell read_csv that the first column is the index column by passing index_col=0 :

In [40]:
pd.read_csv(io.StringIO(df.to_csv()), index_col=0)

Out[40]:
          a         b         c
0  0.109066 -1.112704 -0.545209
1  0.447114  1.525341  0.317252
2  0.507495  0.137863  0.886283
3  1.452867  1.888363  1.168101
4  0.901371 -0.704805  0.088335

Answer 2

This is usually caused by your CSV having been saved along with an (unnamed) index ( RangeIndex ).

(The fix would actually need to be done when saving the DataFrame, but this isn't always an option.)

Workaround: read_csv with index_col=[0] argument

IMO, the simplest solution would be to read the unnamed column as the index . Specify an index_col=[0] argument to pd.read_csv , this reads in the first column as the index. (Note the square brackets).

df = pd.DataFrame('x', index=range(5), columns=list('abc'))
df

   a  b  c
0  x  x  x
1  x  x  x
2  x  x  x
3  x  x  x
4  x  x  x

# Save DataFrame to CSV.
df.to_csv('file.csv')

<!- ->

pd.read_csv('file.csv')

   Unnamed: 0  a  b  c
0           0  x  x  x
1           1  x  x  x
2           2  x  x  x
3           3  x  x  x
4           4  x  x  x

# Now try this again, with the extra argument.
pd.read_csv('file.csv', index_col=[0])

   a  b  c
0  x  x  x
1  x  x  x
2  x  x  x
3  x  x  x
4  x  x  x

Note
You could have avoided this in the first place by using index=False if the output CSV was created in pandas, if your DataFrame does not have an index to begin with:

 df.to_csv('file.csv', index=False)

But as mentioned above, this isn't always an option.

---

Stopgap Solution: Filtering with str.match

If you cannot modify the code to read/write the CSV file, you can just remove the column by filtering with str.match :

df 

   Unnamed: 0  a  b  c
0           0  x  x  x
1           1  x  x  x
2           2  x  x  x
3           3  x  x  x
4           4  x  x  x

df.columns
# Index(['Unnamed: 0', 'a', 'b', 'c'], dtype='object')

df.columns.str.match('Unnamed')
# array([ True, False, False, False])

df.loc[:, ~df.columns.str.match('Unnamed')]
 
   a  b  c
0  x  x  x
1  x  x  x
2  x  x  x
3  x  x  x
4  x  x  x

Answer 3

要获取所有未命名列，您还可以使用正则表达式，例如df.drop(df.filter(regex="Unname"),axis=1, inplace=True)

Answer 4

Another case that this might be happening is if your data was improperly written to your csv to have each row end with a comma. This will leave you with an unnamed column Unnamed: x at the end of your data when you try to read it into a df .

Answer 5

You can do either of the following with 'Unnamed' Columns:

1. Delete unnamed columns
2. Rename them (if you want to use them)

Method 1: Delete Unnamed Columns

# delete one by one like column is 'Unnamed: 0' so use it's name
df.drop('Unnamed: 0', axis=1, inplace=True)

#delete all Unnamed Columns in a single code of line using regex
df.drop(df.filter(regex="Unnamed"),axis=1, inplace=True)

Method 2: Rename Unnamed Columns

df.rename(columns = {'Unnamed: 0':'Name'}, inplace = True)

If you want to write out with a blank header as in the input file, just choose 'Name' above to be ''.

where the OP's input data 'file.csv' was:

,A,B,C
0,1,2,3
1,4,5,6
2,7,8,9

#read file df = pd.read_csv('file.csv')

Answer 6

只需使用以下命令删除该列： del df['column_name']

Answer 7

这样做很简单：

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

Answer 8

或者：

df = df.drop(columns=['Unnamed: 0'])

Answer 9

from IPython.display import display
import pandas as pd
import io


df = pd.read_csv('file.csv',index_col=[0])
df = pd.read_csv(io.StringIO(df.to_csv(index=False)))
display(df.head(5))

Answer 10

A solution that is agnostic to whether the index has been written or not when utilizing df.to_csv() is shown below:

df = pd.read_csv(file_name)
if 'Unnamed: 0' in df.columns:
    df.drop('Unnamed: 0', axis=1, inplace=True)

If an index was not written, then index_col=[0] will utilize the first column as the index which is behavior that one would not want.

Answer 11

In my experience, there are many reasons you might not want to set that column as index_col =[0] as so many people suggest above. For example it might contain jumbled index values because data were saved to csv after being indexed or sorted without df.reset_index(drop=True) leading to instant confusion.

So if you know the file has this column and you don't want it, as per the original question, the simplest 1-line solutions are:

df = pd.read_csv('file.csv').drop(columns=['Unnamed: 0'])

or

df = pd.read_csv('file.csv',index_col=[0]).reset_index(drop=True)