CODEWITHSUNDEEP
pythonpandas
Yalun Gao
Yalun Gao

Reputation: 39

How can I convert a column of dataframe from object to float

Hear is my original dataframe columns type:

#   Column                     Non-Null Count  Dtype 
---  ------                     --------------  ----- 
 0   NAME                       23605 non-null  object
 1   DEPARTMENT_NAME            23605 non-null  object
 2   TITLE                      23605 non-null  object
 3   REGULAR                    21939 non-null  object
 4   RETRO                      13643 non-null  object
 5   OTHER                      13351 non-null  object
 6   OVERTIME                   6826 non-null   object
 7   INJURED                    1312 non-null   object
 8   DETAIL                     2355 non-null   object
 9   QUINN/EDUCATION INCENTIVE  1351 non-null   object
 10  TOTAL EARNINGS             23605 non-null  object
 11  POSTAL                     23605 non-null  object

I want to convert some of them into float type, say Total earnings, I tried:

df['TOTAL EARNINGS'] = df['TOTAL EARNINGS'].astype(int)

and

df['TOTAL EARNINGS'] = pd.to_numeric(df['TOTAL EARNINGS'])

But I got:

ValueError: setting an array element with a sequence.

or

TypeError: Invalid object type at position 0

And I don't know why, is there any other methods to do so? Here is my data: https://data.boston.gov/dataset/418983dc-7cae-42bb-88e4-d56f5adcf869/resource/31358fd1-849a-48e0-8285-e813f6efbdf1/download/employeeearningscy18full.csv

Here are some pictures of my dataframe: enter image description here enter image description here enter image description here

Upvotes: 1

Views: 602

Answers (1)

Amit Kumar Singh
Amit Kumar Singh

Reputation: 4475

This happens because your original data has 2 rows which are completely text.

First execute command below to clean those rows.

df = df[df["TOTAL EARNINGS"]!="TOTAL EARNINGS"]

Then, change the datatype

df['TOTAL EARNINGS'] = df['TOTAL EARNINGS'].astype(float)

You can check datatypes thereafter as 

df.dtypes

Upvotes: 1

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