csv - Python Pandas read_csv skip rows but keep header

Question

I'm having trouble figuring out how to skip n rows in a csv file but keep the header which is the 1 row.

What I want to do is iterate but keep the header from the first row. skiprows makes the header the first row after the skipped rows. What is the best way of doing this?

data = pd.read_csv('test.csv', sep='|', header=0, skiprows=10, nrows=10)

Answer 1

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))

---

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(" ".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