Pandas Convert Data Type from Object to Float

• You can use 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

• For details on how to use it checkout this link :

• 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')
  

• Other option is to use 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)
  

• I have showed you examples because if any of your column won't have a number then it will be converted to NaN... so be careful while using it.

I tried all methods suggested here but sadly none worked. Instead, found this to be working:

df['column'] = pd.to_numeric(df['column'],errors = 'coerce')

And then check it using:

print(df.info())

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.

You can try the following:

df['column'] = df['column'].map(lambda x: float(x))

First check your data cuz you may get an error if you have ',' instead of '.' if so, you need to transform every ',' into '.' with a function :

def replacee(s):
i=str(s).find(',')
if(i>0):
    return s[:i] + '.' + s[i+1:]
else :
    return s 

then you need to apply this function on every row in your column :

dfOPA['Montant']=dfOPA['Montant'].apply(replacee)

then the convert function will work fine :

dfOPA['Montant'] = pd.to_numeric(dfOPA['Montant'],errors = 'coerce')

Eg, For Converting $40,000.00 object to 40000 int or float32

Follow this step by step :

$40,000.00 ---(**1**. remove $)--->  40,000.00  ---(**2**. remove , comma)--->  40000.00 ---(**3**. remove . dot)--->  4000000  ---(**4**. remove empty space)---> 4000000  ---(**5**. Remove NA Values)--->  4000000 ---(**6**. now this is object type so, convert to int using .astype(int) )---> 4000000  ---(**7**. divide by 100)---> 40000  

Implementing code In Pandas

table1["Price"] = table1["Price"].str.replace('$','')<br>
table1["Price"] = table1["Price"].str.replace(',','')<br>
table1["Price"] = table1["Price"].str.replace('.','')<br>
table1["Price"] = table1["Price"].str.replace(' ','')     
table1 = table1.dropna()<br>
table1["Price"] = table1["Price"].astype(int)<br>
table1["Price"] = table1["Price"] / 100<br> 

Finally it's done