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I've the following dataframe
df1 = df[['tripduration','starttime','stoptime','start station name','end station name','bikeid','usertype','birth year','gender']] print(df1.head(2)) which prints the following
tripduration starttime stoptime start station name \ 0 364 2017-09-01 00:02:01 2017-09-01 00:08:05 Exchange Place 1 357 2017-09-01 00:08:12 2017-09-01 00:14:09 Warren St end station name bikeid usertype birth year gender 0 Marin Light Rail 29670 Subscriber 1989.0 1 1 Newport Pkwy 26163 Subscriber 1980.0 1 I am using the following code to convert "birth year" column type from float to int.
df1[['birth year']] = df1[['birth year']].astype(int) print df1.head(2) But I get the following error. How to fix this?
ValueErrorTraceback (most recent call last) <ipython-input-25-0fe766e4d4a7> in <module>() ----> 1 df1[['birth year']] = df1[['birth year']].astype(int) 2 print df1.head(2) 3 __zeppelin__._displayhook() /usr/miniconda2/lib/python2.7/site-packages/pandas/util/_decorators.pyc in wrapper(*args, **kwargs) 116 else: 117 kwargs[new_arg_name] = new_arg_value --> 118 return func(*args, **kwargs) 119 return wrapper 120 return _deprecate_kwarg /usr/miniconda2/lib/python2.7/site-packages/pandas/core/generic.pyc in astype(self, dtype, copy, errors, **kwargs) 4002 # else, only a single dtype is given 4003 new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors, -> 4004 **kwargs) 4005 return self._constructor(new_data).__finalize__(self) 4006 /usr/miniconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in astype(self, dtype, **kwargs) 3460 3461 def astype(self, dtype, **kwargs): -> 3462 return self.apply('astype', dtype=dtype, **kwargs) 3463 3464 def convert(self, **kwargs): /usr/miniconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs) 3327 3328 kwargs['mgr'] = self -> 3329 applied = getattr(b, f)(**kwargs) 3330 result_blocks = _extend_blocks(applied, result_blocks) 3331 /usr/miniconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in astype(self, dtype, copy, errors, values, **kwargs) 542 def astype(self, dtype, copy=False, errors='raise', values=None, **kwargs): 543 return self._astype(dtype, copy=copy, errors=errors, values=values, --> 544 **kwargs) 545 546 def _astype(self, dtype, copy=False, errors='raise', values=None, /usr/miniconda2/lib/python2.7/site-packages/pandas/core/internals.pyc in _astype(self, dtype, copy, errors, values, klass, mgr, **kwargs) 623 624 # _astype_nansafe works fine with 1-d only --> 625 values = astype_nansafe(values.ravel(), dtype, copy=True) 626 values = values.reshape(self.shape) 627 /usr/miniconda2/lib/python2.7/site-packages/pandas/core/dtypes/cast.pyc in astype_nansafe(arr, dtype, copy) 685 686 if not np.isfinite(arr).all(): --> 687 raise ValueError('Cannot convert non-finite values (NA or inf) to ' 688 'integer') 689 ValueError: Cannot convert non-finite values (NA or inf) to integer 
938
Fix Cannot convert non-finite values (NA or inf) to integer using fillna() To solve this error, we can replace all the nan values in the “Marks” column with zero or a value of your choice like fillna(100) by using the fillna(0) method pf the pandas data frame.
The np. isfinite() function tests element-wise, whether it is finite (not infinity or not, Not a Number), and returns the result as the boolean array. You can pass arrays also to check whether the elements present in the array belong to a finite class. If it is not finite, the method returns False otherwise True.
The astype() method returns a new DataFrame where the data types has been changed to the specified type. You can cast the entire DataFrame to one specific data type, or you can use a Python Dictionary to specify a data type for each column, like this: { 'Duration': 'int64', 'Pulse' : 'float', 'Calories': 'int64' }
If your DF is big, you're probably not seeing the missing numbers. But you can use the fillna function to help
>>> df = pd.DataFrame(data=data, columns=['id', 'birth_year']) >>> df id birth_year 0 1 1989.0 1 2 1990.0 2 3 NaN >>> df.birth_year 0 1989.0 1 1990.0 2 NaN Name: birth_year, dtype: float64 >>> df.birth_year.astype(int) ERROR |2018.01.29T18:14:04|default:183: Unhandled Terminal Exception Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/util/_decorators.py", line 91, in wrapper return func(*args, **kwargs) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/generic.py", line 3410, in astype **kwargs) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/internals.py", line 3224, in astype return self.apply('astype', dtype=dtype, **kwargs) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/internals.py", line 3091, in apply applied = getattr(b, f)(**kwargs) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/internals.py", line 471, in astype **kwargs) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/internals.py", line 521, in _astype values = astype_nansafe(values.ravel(), dtype, copy=True) File "/usr/local/devtools/uat/anaconda4321/lib/python3.6/site- packages/pandas/core/dtypes/cast.py", line 620, in astype_nansafe raise ValueError('Cannot convert non-finite values (NA or inf) to ' ValueError: Cannot convert non-finite values (NA or inf) to integer >>> df = df.fillna(0) >>> df.birth_year.astype(int) 0 1989 1 1990 2 0 Name: birth_year, dtype: int64 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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