How to Get Rid of "Unnamed: 0" Column in a Pandas Dataframe Read in from CSV File

How to get rid of Unnamed: 0 column in a pandas DataFrame read in from CSV file?

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

Remove Unnamed columns in 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    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

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NOTE: very often there is only one unnamed column Unnamed: 0, which is the first column in the CSV file. This is the result of the following steps:

1. a DataFrame is saved into a CSV file using parameter index=True, which is the default behaviour
2. we read this CSV file into a DataFrame using pd.read_csv() without explicitly specifying index_col=0 (default: index_col=None)

The easiest way to get rid of this column is to specify the parameter pd.read_csv(..., index_col=0):

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

pd.read_csv add column named Unnamed: 0

You should try:

pd.read_csv('file.csv', index_col=0)

index_col : int or sequence or False, default None Column to use as
the row labels of the DataFrame. If a sequence is given, a MultiIndex
is used. If you have a malformed file with delimiters at the end of
each line, you might consider index_col=False to force pandas to not
use the first column as the index (row names)

Example Dataset:

I have taken the dataset from google,So while i'm simply trying to import the data with pd.read_csv it shows the Unnamed: 0 as default.

>>> df = pd.read_csv("amis.csv")
>>> df.head()
   Unnamed: 0  speed  period  warning  pair
0           1     26       1        1     1
1           2     26       1        1     1
2           3     26       1        1     1
3           4     26       1        1     1
4           5     27       1        1     1

So, Just to avoid the the Unnamed: 0 we have to use index_col=0 and will get the nicer dataframe:

>>> df = pd.read_csv("amis.csv", index_col=0)
>>> df.head()
   speed  period  warning  pair
1     26       1        1     1
2     26       1        1     1
3     26       1        1     1
4     26       1        1     1
5     27       1        1     1

Note : So, to make it more explicit to understand when we say index_col=0, it placed the first column as the index in the dataFrame rather appearing as Unnamed: 0 .

Hope this will help.

Python Pandas 'Unnamed' column keeps appearing

each time I run my program (...) a new column shows up called 'Unnamed'.

I suppose that's due to reset_index or maybe you have a to_csv somewhere in your code as @jpp suggested. To fix the to_csv be sure to use index=False:

df.to_csv(path, index=False)

just wanted the 'Subreddit' and 'Appearances' columns

In general, here's how I would approach your task.

What this does is to count all appearances first (keyed by e), and from these counts create a new dataframe to merge with the one you already have (how='outer' adds rows that don't exist yet). This avoids resetting the index for each element which should avoid the problem and is also more performant.

Here's the code with these thoughts included:

base_df = pd.read_csv(location)
appearances = Counter()  # from collections
while counter < 50:
    #gets just the subreddit name
    e = str(elem[counter].get_attribute("href"))
    e = e.replace("https://www.reddit.com/r/", "")
    e = e[:-1]
    appearances[e] += 1
    counter = counter + 2
appearances_df = pd.DataFrame({'e': e, 'appearances': c } 
                               for e, c in x.items())
df = base_df.merge(appearances_df, how='outer', on='e')
    

Python/Pandas - Remove the first row with Unnamed: 0,Unnamed: 1,Unnamed: 2,Unnamed: 3,Unnamed: 4,Unnamed: 5,Unnamed: 6,Unnamed: 7

You need to use the skiprows argument inside the pd.read_excel function to correctly get the column names in the 5th row.

UPDATE Including the forward filling

import pandas as pd

xl = pd.ExcelFile('Sample_File.xlsm')

for sheet in xl.sheet_names:
    df = pd.read_excel(xl, sheet_name=sheet, skiprows=4) # no more iloc here
    df['Comment'] = df['Comment'].ffill()
    df.to_csv(f'{sheet}.csv', index=False)

how should i read a csv file without the 'unnamed' row with pandas?

to_csv() writes an index per default, so you can either disable index when saving your CSV:

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

or specify an index column when reading:

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

How to read csv file in pandas if the pathname contains single inverted comma and i get error?

use " rather than ' for the bonds of the string, like so

df1 = pd.read_csv(r"C:\Users\YUNUS'S LAPTOP\Desktop\Book1.csv")

Pandas merge how to avoid unnamed column

In summary, what you're doing is saving the index to file and when you're reading back from the file, the column previously saved as index is loaded as a regular column.

There are a few ways to deal with this:

Method 1

When saving a pandas.DataFrame to disk, use index=False like this:

df.to_csv(path, index=False)

Method 2

When reading from file, you can define the column that is to be used as index, like this:

df = pd.read_csv(path, index_col='index')

Method 3

If method #2 does not suit you for some reason, you can always set the column to be used as index later on, like this:

df.set_index('index', inplace=True)

After this point, your datafame should look like this:

        userid    locale    age
index
    0    A1092     EN-US     31
    1    B9032     SV-SE     23

I hope this helps.

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