Describe the bug
When data is loaded from a file locally, DataFrame treats all values as string types (empty string) due to which "missing values" are not differentiated/tracked, due to which fillMissingValues() doesn't work as expected.
However, this works when the DataFrame is prepared on the fly (using new DataFrame(...))
To Reproduce
Steps to reproduce the behavior:
Copy the content below to a file & save (for e.g. test.csv)
name,age
Adam,10
Amy,
Run the code below (assuming DataFrame dependency is added):
**Expected behavior**
Second row should have been updated with `0` as the age.
**Screenshots**
<img src="https://user-images.githubusercontent.com/18084419/115605739-81ec3300-a300-11eb-9057-a26c96c2ed87.png" width="250" height="150">
**Desktop (please complete the following information):**
- OS: Ubuntu
**Additional context**
This works as expected when the data is prepared in-house, i.e.
_Code:_
Describe the bug When data is loaded from a file locally, DataFrame treats all values as string types (empty string) due to which "missing values" are not differentiated/tracked, due to which
fillMissingValues()doesn't work as expected.However, this works when the DataFrame is prepared on the fly (using new DataFrame(...))
To Reproduce Steps to reproduce the behavior:
Copy the content below to a file & save (for e.g. test.csv)
Run the code below (assuming DataFrame dependency is added):
(async() => { const df = await DataFrame.fromCSV('test.csv'); df.fillMissingValues(0).show(); })();
const DataFrame = require('dataframe-js').DataFrame;
(async() => { const df = await new DataFrame([ { name: 'Adam', age: 10 }, { name: 'Amy' } ]); df.fillMissingValues(0).show(); })();