import numpy as np
import pandas as pd
df = pd.read_csv("/Users/rajacsp/datasets/sales_data_types.csv")
|
Customer Number |
Customer Name |
2016 |
2017 |
Percent Growth |
Jan Units |
Month |
Day |
Year |
Active |
0 |
10002.0 |
Quest Industries |
$125,000.00 |
$162500.00 |
30.00% |
500 |
1 |
10 |
2015 |
Y |
1 |
552278.0 |
Smith Plumbing |
$920,000.00 |
$101,2000.00 |
10.00% |
700 |
6 |
15 |
2014 |
Y |
2 |
23477.0 |
ACME Industrial |
$50,000.00 |
$62500.00 |
25.00% |
125 |
3 |
29 |
2016 |
Y |
3 |
24900.0 |
Brekke LTD |
$350,000.00 |
$490000.00 |
4.00% |
75 |
10 |
27 |
2015 |
Y |
4 |
651029.0 |
Harbor Co |
$15,000.00 |
$12750.00 |
-15.00% |
Closed |
2 |
2 |
2014 |
N |
Customer Number float64
Customer Name object
2016 object
2017 object
Percent Growth object
Jan Units object
Month int64
Day int64
Year int64
Active object
dtype: object
df['Customer Number'].astype('int')
0 10002
1 552278
2 23477
3 24900
4 651029
Name: Customer Number, dtype: int64
Customer Number float64
Customer Name object
2016 object
2017 object
Percent Growth object
Jan Units object
Month int64
Day int64
Year int64
Active object
dtype: object