CODEWITHSUNDEEP
pythonpandas
user308827
user308827

Reputation: 21971

Removing header column from pandas dataframe

I have the foll. dataframe:

df

   A   B
0  23  12
1  21  44
2  98  21

How do I remove the column names A and B from this dataframe? One way might be to write it into a csv file and then read it in specifying header=None. is there a way to do that without writing out to csv and re-reading?

Upvotes: 51

Views: 304161

Answers (7)

Anirudh Maurya
Anirudh Maurya

Reputation: 1

Removing header column from pandas DataFrame. It can be done without writing out to csv and then reading again.

Solution:

Replace the column names by first row of the DataFrame.

df.columns = df.iloc[0,:].values

and then delete that first row of DataFrame.

df = df.tail(-1)

Upvotes: 0

Peter Moses
Peter Moses

Reputation: 2137

This works perfectly:

To get the dataframe without the header use:

totalRow = len(df.index)
df.iloc[1: totalRow]

Or you can use the second method like this:

totalRow = df.index.stop
df.iloc[1, totalRow]

Upvotes: 0

lalitjoesat
lalitjoesat

Reputation: 11

You can first convert the DataFrame to an Numpy array, using this:

s1=df.iloc[:,0:2].values

Then, convert the numpy array back to DataFrame:

s2=pd.DataFrame(s1)

This will return a DataFrame with no Columns. enter image description here

Upvotes: 1

wilma88
wilma88

Reputation: 181

I had the same problem but solved it in this way:

df = pd.read_csv('your-array.csv', skiprows=[0])

Upvotes: 14

Kangand
Kangand

Reputation: 19

Haven't seen this solution yet so here's how I did it without using read_csv:

df.rename(columns={'A':'','B':''})

If you rename all your column names to empty strings your table will return without a header.

And if you have a lot of columns in your table you can just create a dictionary first instead of renaming manually:

df_dict = dict.fromkeys(df.columns, '')
df.rename(columns = df_dict)

Upvotes: 1

Max
Max

Reputation: 1805

How to get rid of a header(first row) and an index(first column).

To write to CSV file:

df = pandas.DataFrame(your_array)
df.to_csv('your_array.csv', header=False, index=False)

To read from CSV file:

df = pandas.read_csv('your_array.csv')
a = df.values

If you want to read a CSV file that doesn't contain a header, pass additional parameter header:

df = pandas.read_csv('your_array.csv', header=None)

Upvotes: 26

jezrael
jezrael

Reputation: 862661

I think you cant remove column names, only reset them by range with shape:

print df.shape[1]
2

print range(df.shape[1])
[0, 1]

df.columns = range(df.shape[1])
print df
    0   1
0  23  12
1  21  44
2  98  21

This is same as using to_csv and read_csv:

print df.to_csv(header=None,index=False)
23,12
21,44
98,21

print pd.read_csv(io.StringIO(u""+df.to_csv(header=None,index=False)), header=None)
    0   1
0  23  12
1  21  44
2  98  21

Next solution with skiprows:

print df.to_csv(index=False)
A,B
23,12
21,44
98,21

print pd.read_csv(io.StringIO(u""+df.to_csv(index=False)), header=None, skiprows=1)
    0   1
0  23  12
1  21  44
2  98  21

Upvotes: 56

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