Check Datatypes
Sat 17 May 2025
title: "Check Data Types" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
df = pd.read_csv("/Users/rajacsp/datasets/sales_data_types.csv")
df
| 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 |
df.dtypes
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
df.dtypes
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
Score: 5
Category: data-wrangling