I read some weather data from a csv file as a dataframe named "weather". The problem is that one of the columns' data type is an object. this is weird beacuse it indicates temperature... anyway, how to I change it to a float? I tried to_numeric but it can't parse it.
weather.info()
weather.head()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 304 entries, 2017-01-01 to 2017-10-31
Data columns (total 2 columns):
Temp 304 non-null object
Rain 304 non-null float64
dtypes: float64(1), object(1)
memory usage: 17.1+ KB
Temp Rain
Date
2017-01-01 12.4 0.0
2017-02-01 11 0.6
2017-03-01 10.4 0.6
2017-04-01 10.9 0.2
2017-05-01 13.2 0.0
I eventually used:
weather["Temp"] = weather["Temp"].convert_objects(convert_numeric=True)
It worked just fine, except that I got the following message.
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:3: FutureWarning:
convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.
pandas.Series.astype
You can do something like this :
weather["Temp"] = weather.Temp.astype(float)
You can also use pd.to_numeric
that will convert the column from object to float
Example :
s = pd.Series(['apple', '1.0', '2', -3])
print(pd.to_numeric(s, errors='ignore'))
print("=========================")
print(pd.to_numeric(s, errors='coerce'))
Output:
0 apple
1 1.0
2 2
3 -3
=========================
dtype: object
0 NaN
1 1.0
2 2.0
3 -3.0
dtype: float64
In your case you can do something like this:
weather["Temp"] = pd.to_numeric(weather.Temp, errors='coerce')
convert_objects
Example is as follows
>> pd.Series([1,2,3,4,'.']).convert_objects(convert_numeric=True)
0 1
1 2
2 3
3 4
4 NaN
dtype: float64
You can use this as follows:
weather["Temp"] = weather.Temp.convert_objects(convert_numeric=True)
NaN
... so be careful while using it.Source: Stackoverflow.com