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Analyzing and Visualizing Real-World Data
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Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
Loading the DataData TypesColumns OverviewRemoving Characters: Method 1Removing Characters: Method 2Checking ColumnsReplacing Characters
2. Preprocessing Data: Part II
Analyzing ColumnsRemoving and ReplacingProcessing DatesWrong Convertation?Proper ConvertingThat's All?
3. Analyzing Data
Superficial AnalysisThe Most Profitable StoreDate RangeHow Periodic the Data is?The Most Profitable PeriodsThe Best CombinationHolidays EffectWhat Holidays are we Considering?The Most Profitable Holiday
4. Visualizing Data
First Look at the DataComparing DynamicsFinding RelationDoes Temperature Affect?Or is it Holiday Effect?Comparing ShopsAdding Holidays to Comparison

bookRemoving 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 .str accessor.

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, where type is either int for integers or float for real numbers. For instance, df['column'] = df['column'].astype(int);
  • The second method is to use the .to_numeric() method of pd (pandas), passing the column as the parameter. For instance, df['column'] = pd.to_numeric(df['column']).

Tarea

  1. Import the pandas library with the pd alias.
  2. Read the csv file and save it as a dataframe in the df variable.
  3. Remove the redundant symbols from prices and convert them to float type:
    • Select the 'Fuel_Price' column;
    • Use the .str accessor;
    • 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 of df.
  4. Remove the % symbols from the 'Unemployment' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  5. Remove the °C symbols from the 'Temperature' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  6. Display the first row of the df dataframe and the data types of the df dataframe.

Once you've completed this task, click the button below the code to check your solution.

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Sección 1. Capítulo 4
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bookRemoving 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 .str accessor.

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, where type is either int for integers or float for real numbers. For instance, df['column'] = df['column'].astype(int);
  • The second method is to use the .to_numeric() method of pd (pandas), passing the column as the parameter. For instance, df['column'] = pd.to_numeric(df['column']).

Tarea

  1. Import the pandas library with the pd alias.
  2. Read the csv file and save it as a dataframe in the df variable.
  3. Remove the redundant symbols from prices and convert them to float type:
    • Select the 'Fuel_Price' column;
    • Use the .str accessor;
    • 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 of df.
  4. Remove the % symbols from the 'Unemployment' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  5. Remove the °C symbols from the 'Temperature' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  6. Display the first row of the df dataframe and the data types of the df dataframe.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 4
toggle bottom row

bookRemoving 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 .str accessor.

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, where type is either int for integers or float for real numbers. For instance, df['column'] = df['column'].astype(int);
  • The second method is to use the .to_numeric() method of pd (pandas), passing the column as the parameter. For instance, df['column'] = pd.to_numeric(df['column']).

Tarea

  1. Import the pandas library with the pd alias.
  2. Read the csv file and save it as a dataframe in the df variable.
  3. Remove the redundant symbols from prices and convert them to float type:
    • Select the 'Fuel_Price' column;
    • Use the .str accessor;
    • 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 of df.
  4. Remove the % symbols from the 'Unemployment' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  5. Remove the °C symbols from the 'Temperature' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  6. Display the first row of the df dataframe and the data types of the df dataframe.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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 .str accessor.

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, where type is either int for integers or float for real numbers. For instance, df['column'] = df['column'].astype(int);
  • The second method is to use the .to_numeric() method of pd (pandas), passing the column as the parameter. For instance, df['column'] = pd.to_numeric(df['column']).

Tarea

  1. Import the pandas library with the pd alias.
  2. Read the csv file and save it as a dataframe in the df variable.
  3. Remove the redundant symbols from prices and convert them to float type:
    • Select the 'Fuel_Price' column;
    • Use the .str accessor;
    • 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 of df.
  4. Remove the % symbols from the 'Unemployment' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  5. Remove the °C symbols from the 'Temperature' column (which are located on the right side), using the .rstrip() method, and convert the values to float format using the same algorithm as in step 3.
  6. Display the first row of the df dataframe and the data types of the df dataframe.

Once you've completed this task, click the button below the code to check your solution.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 4
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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