This recipe includes the following topics:

Note:
– read_csv() returns a Pandas DataFrame
– The syntax for both reading a local file and an external file is the same

---

# import module
from pandas import read_csv

filename = 'pima-indians-diabetes.data.csv'

# define column names
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

# load file as a Pandas DataFrame
pimaDf = read_csv(filename, names=cols)

# display row, column size
print(pimaDf.shape)

# display first 5 rows
print(pimaDf.head(5))

(768, 9)
   preg  plas  pres  skin  test  mass   pedi  age  class
0     6   148    72    35     0  33.6  0.627   50      1
1     1    85    66    29     0  26.6  0.351   31      0
2     8   183    64     0     0  23.3  0.672   32      1
3     1    89    66    23    94  28.1  0.167   21      0
4     0   137    40    35   168  43.1  2.288   33      1

# import module
from pandas import read_csv

fileGitURL = 'https://raw.githubusercontent.com/andrewgurung/data-repository/master/pima-indians-diabetes.data.csv'

# define column names
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

# load file as a Pandas DataFrame
pimaDf = read_csv(fileGitURL, names=cols)

# display row, column size
print(pimaDf.shape)

(768, 9)