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Unfurtunately i cant provide the data iam using (since it's enterprise privative), but it has a lot of NaN values.
I do:
df = df.dropna(axis=0,thresh=1)
Returns a DataFrame with nan values, i can confirm this looping through the DataFrame and applying np.isnan.
Then i do:
df = df.fillna(0)
The new DataFrame has no more NaN. Why is this that dropna cant see all NaNs and fillna can ?
Thankyou in advance.
#### Problem description
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]
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#### Expected Output
#### Output of ``pd.show_versions()``
<details>
[paste the output of ``pd.show_versions()`` here below this line]
</details>
The text was updated successfully, but these errors were encountered:
On Thu, May 23, 2019 at 3:11 PM gustavolinari ***@***.***> wrote:
Probably because of the thresh parameter. Remove it and all NaN will be
gone.
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Unfurtunately i cant provide the data iam using (since it's enterprise privative), but it has a lot of NaN values.
I do:
df = df.dropna(axis=0,thresh=1)
Returns a DataFrame with nan values, i can confirm this looping through the DataFrame and applying np.isnan.
Then i do:
df = df.fillna(0)
The new DataFrame has no more NaN. Why is this that dropna cant see all NaNs and fillna can ?
Thankyou in advance.
The text was updated successfully, but these errors were encountered: