CODEWITHSUNDEEP
pythonpandasmachine-learningscikit-learnk-means
Mitch
Mitch

Reputation: 69

Python - Input contains NaN, infinity or a value too large for dtype('float64')

I am new on Python. I am trying to use sklearn.cluster. Here is my code:

from sklearn.cluster import MiniBatchKMeans

kmeans=MiniBatchKMeans(n_clusters=2)
kmeans.fit(df)

But I get the following error:

     50             and not np.isfinite(X).all()):
     51         raise ValueError("Input contains NaN, infinity"
---> 52                          " or a value too large for %r." % X.dtype)

 ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

I checked that the there is no Nan or infinity value. So there is only one option left. However, my data info tells me that all variables are float64, so I don't understand where the problem comes from.

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 362358 entries, 135 to 4747145
Data columns (total 8 columns):
User         362358 non-null float64
Hour         362352 non-null float64
Minute       362352 non-null float64
Day          362352 non-null float64
Month        362352 non-null float64
Year         362352 non-null float64
Latitude     362352 non-null float64
Longitude    362352 non-null float64
dtypes: float64(8)
memory usage: 24.9 MB

Thanks a lot,

Upvotes: 5

Views: 13004

Answers (3)

Max Kleiner
Max Kleiner

Reputation: 1622

By looking at your df.info(), it appears that there are 6 more non-null Users values than there are values of any other column. This would indicate that you have 6 nulls in each of the other columns, and that is the reason for the error.

So you can slice your data to the right fit with iloc():

df = pd.read_csv(location1, encoding = "ISO-8859-1").iloc[2:20]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18 entries, 2 to 19
Data columns (total 6 columns):
zip_code     18 non-null int64
latitude     18 non-null float64
longitude    18 non-null float64
city         18 non-null object
state        18 non-null object
county       18 non-null object
dtypes: float64(2), int64(1), object(3)

Upvotes: 1

David Maust
David Maust

Reputation: 8270

By looking at your df.info(), it appears that there are 6 more non-null Users values than there are values of any other column. This would indicate that you have 6 nulls in each of the other columns, and that is the reason for the error.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 362358 entries, 135 to 4747145
Data columns (total 8 columns):
User         362358 non-null float64
Hour         362352 non-null float64
Minute       362352 non-null float64
Day          362352 non-null float64
Month        362352 non-null float64
Year         362352 non-null float64
Latitude     362352 non-null float64
Longitude    362352 non-null float64
dtypes: float64(8)
memory usage: 24.9 MB

Upvotes: 2

Fabio Lamanna
Fabio Lamanna

Reputation: 21584

I think that fit() accepts only "array-like, shape = [n_samples, n_features]", not pandas dataframes. So try to pass the values of the dataframe into it as:

kmeans=MiniBatchKMeans(n_clusters=2)
kmeans.fit(df.values)

Or shape them in order to run the function correctly. Hope that helps.

Upvotes: 1

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