Python Pandas read_csv skip rows but keep header

You can pass a list of row numbers to skiprows instead of an integer.

By giving the function the integer 10, you’re just skipping the first 10 lines.

To keep the first row 0 (as the header) and then skip everything else up to row 10, you can write:

pd.read_csv('test.csv', sep='|', skiprows=range(1, 10))

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Other ways to skip rows using read_csv

The two main ways to control which rows read_csv uses are the header or skiprows parameters.

Supose we have the following CSV file with one column:

a
b
c
d
e
f

In each of the examples below, this file is f = io.StringIO("\n".join("abcdef")).

• Read all lines as values (no header, defaults to integers)

  >>> pd.read_csv(f, header=None)
     0
  0  a
  1  b
  2  c
  3  d
  4  e
  5  f
  

• Use a particular row as the header (skip all lines before that):

  >>> pd.read_csv(f, header=3)
     d
  0  e
  1  f
  

• Use a multiple rows as the header creating a MultiIndex (skip all lines before the last specified header line):

  >>> pd.read_csv(f, header=[2, 4])                                                                                                                                                                        
     c
     e
  0  f
  

• Skip N rows from the start of the file (the first row that’s not skipped is the header):

  >>> pd.read_csv(f, skiprows=3)                                                                                                                                                                      
     d
  0  e
  1  f
  

• Skip one or more rows by giving the row indices (the first row that’s not skipped is the header):

  >>> pd.read_csv(f, skiprows=[2, 4])                                                                                                                                                                      
     a
  0  b
  1  d
  2  f