In Pandas, whats the equivalent of 'nrows' from read_csv() to be used in read_excel()?

If you know the number of rows in your Excel sheet, you can use the skip_footer parameter to read the first n - skip_footer rows of your file, where n is the total number of rows.

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_excel.html

Usage:

data = pd.read_excel(filepath, header=0, parse_cols = "A:D", skip_footer=80)

Assuming your excel sheet has 100 rows, this line would parse the first 20 rows.

As noted in the documentation, as of pandas version 0.23, this is now a built-in option, and functions almost exactly as the OP stated.

The code

data = pd.read_excel(filepath, header=0, skiprows=4, nrows= 20, use_cols = "A:D")

will now read the excel file, take data from the first sheet (default), skip 4 rows of data, then take the first line (i.e., the fifth line of the sheet) as the header, read the next 20 rows of data into the dataframe (lines 6-25), and only use the columns A:D. Note that use_cols is now the final option, as parse_cols is deprecated.

I'd like to make (extend) @Erol's answer bit more flexible.

Assuming that we DON'T know the total number of rows in the excel sheet:

xl = pd.ExcelFile(filepath)

# parsing first (index: 0) sheet
total_rows = xl.book.sheet_by_index(0).nrows

skiprows = 4
nrows = 20

# calc number of footer rows
# (-1) - for the header row
skipfooter = total_rows - nrows - skiprows - 1

df = xl.parse(0, skiprows=skiprows, skipfooter=skipfooter, parse_cols="A:D") \
       .dropna(axis=1, how='all')

.dropna(axis=1, how='all') will drop all columns containing only NaN's