Creating a List and maintaining integer value-CodePudding

I am new to python a bit.

I am trying to convert a dataframe to list after changing the datatype of a particular column to integer. The funny thing is when converted to list, the column still has float.

There are three columns in the dataframe, first two is float and I want the last to be integer, but it still comes as float.

If I change all to integer, then the list creates as integer.

0   1.53    3.13    0.0
1   0.58    2.83    0.0
2   0.28    2.69    0.0 
3   1.14    2.14    0.0
4   1.46    3.39    0.0
... ... ... ...

495 2.37 0.93 1.0 496 2.85 0.52 1.0 497 2.35 0.39 1.0 498 2.96 1.68 1.0 499 2.56 0.16 1.0

Above is the Dataframe.

Below is the last column converted

#convert last column to integer datatype
data[6] = data[6].astype(dtype ='int64')
display(data.dtypes)

The below is converting the dataframe to list.

#Turn DF to list
data_to_List = data.values.tolist()
data_to_List

#below is what is shown now.

[[1.53, 3.13, 0.0],
[0.58, 2.83, 0.0],
[0.28, 2.69, 0.0],
[1.14, 2.14, 0.0],
[3.54, 0.75, 1.0],
[3.04, 0.15, 1.0],
[2.49, 0.15, 1.0],
[2.27, 0.39, 1.0],
[3.65, 1.5, 1.0],

I want the last column to be just 0 or 1 and not 0.0 or 1.0

CodePudding user response：

Yes, you are correct pandas is converting int to float when you use data.values

You can convert your float to int by using the below list comprehension:

data_to_List = [[x[0],x[1],int(x[2])] for x in data.values.tolist()]

print(data_to_List)

[[1.53, 3.13, 0],
 [0.58, 2.83, 0],
 [0.28, 2.69, 0],
 [1.14, 2.14, 0],
 [1.46, 3.39, 0]]