I want to delete to just column name (x,y,z), and use only data.
In [68]: df
Out[68]:
x y z
0 1 0 1
1 2 0 0
2 2 1 1
3 2 0 1
4 2 1 0
I want to print result to same as below.
Out[68]:
0 1 0 1
1 2 0 0
2 2 1 1
3 2 0 1
4 2 1 0
Is it possible? How can I do this?
First, write ALTER TABLE , followed by the name of the table you want to change (in our example, product ). Next add the DROP COLUMN clause, followed by the name of the column you want to remove (in our example, description ). This removes the column from the table, including any data stored in it.
In R, the easiest way to remove columns from a data frame based on their name is by using the %in% operator. This operator lets you specify the redundant column names and, in combination with the names() function, removes them from the data frame. Alternatively, you can use the subset() function or the dplyr package.
In pandas by default need column names.
But if really want 'remove'
columns what is strongly not recommended, because get duplicated column names is possible assign empty strings:
df.columns = [''] * len(df.columns)
But if need write df
to file without columns and index add parameter header=False
and index=False
to to_csv
or to_excel
.
df.to_csv('file.csv', header=False, index=False)
df.to_excel('file.xlsx', header=False, index=False)
If all you need is to print out without the headers then you can use the to_string()
and set header=False
, e.g.:
>>> print(df.to_string(header=False))
0 1 0 1
1 2 0 0
2 2 1 1
3 2 0 1
4 2 1 0
If you need to remove the header alone, uses '.values'.
df = df[:].values
But the above code will return a numpy array instead of dataframe. Converting the same again into dataframe will add default values to column names (0,1..).
First find the number of columns by:
df.shape # it helps you to know the total no of columns you have (as well as rows)
Lets say shape is (188,8)
:
df.columns = np.arange(8) #here we have `8` columns
This makes the columns as int64 starting from 0 to 7 .
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