Removing Characters: Method 1
There are at least two different ways to solve the problem of redundant symbols. The first method is to treat the column values as strings and then apply the necessary string method to remove the redundant characters.
Note
To treat column values as strings, use the
.straccessor.
After deleting symbols, we can convert the columns into numerical format.
There are at least two ways to do this:
- The first method is to use the
.astype(type)method on a column, wheretypeis eitherintfor integers orfloatfor real numbers. For instance,df['column'] = df['column'].astype(int); - The second method is to use the
.to_numeric()method ofpd(pandas), passing the column as the parameter. For instance,df['column'] = pd.to_numeric(df['column']).
Swipe to start coding
-
Import the
pandaslibrary with thepdalias. -
Read the
csvfile and save it as a dataframe in thedfvariable. -
Remove the redundant symbols from prices and convert them to
floattype:- Select the
'Fuel_Price'column; - Use the
.straccessor; - Remove the
'$'characters from the left using the.lstrip()function; - Convert the resulting values to numerical format (
float) using the.astype()method; - Assign the result to the
'Fuel_Price'column ofdf.
- Select the
-
Remove the
%symbols from the'Unemployment'column (which are located on the right side), using the.rstrip()method, and convert the values tofloatformat using the same algorithm as in step 3. -
Remove the
°Csymbols from the'Temperature'column (which are located on the right side), using the.rstrip()method, and convert the values tofloatformat using the same algorithm as in step 3. -
Display the first row of the
dfdataframe and the data types of thedfdataframe.
Once you've completed this task, click the button below the code to check your solution.
Oplossing
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There are at least two different ways to solve the problem of redundant symbols. The first method is to treat the column values as strings and then apply the necessary string method to remove the redundant characters.
Note
To treat column values as strings, use the
.straccessor.
After deleting symbols, we can convert the columns into numerical format.
There are at least two ways to do this:
- The first method is to use the
.astype(type)method on a column, wheretypeis eitherintfor integers orfloatfor real numbers. For instance,df['column'] = df['column'].astype(int); - The second method is to use the
.to_numeric()method ofpd(pandas), passing the column as the parameter. For instance,df['column'] = pd.to_numeric(df['column']).
Swipe to start coding
-
Import the
pandaslibrary with thepdalias. -
Read the
csvfile and save it as a dataframe in thedfvariable. -
Remove the redundant symbols from prices and convert them to
floattype:- Select the
'Fuel_Price'column; - Use the
.straccessor; - Remove the
'$'characters from the left using the.lstrip()function; - Convert the resulting values to numerical format (
float) using the.astype()method; - Assign the result to the
'Fuel_Price'column ofdf.
- Select the
-
Remove the
%symbols from the'Unemployment'column (which are located on the right side), using the.rstrip()method, and convert the values tofloatformat using the same algorithm as in step 3. -
Remove the
°Csymbols from the'Temperature'column (which are located on the right side), using the.rstrip()method, and convert the values tofloatformat using the same algorithm as in step 3. -
Display the first row of the
dfdataframe and the data types of thedfdataframe.
Once you've completed this task, click the button below the code to check your solution.
Oplossing
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