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Pandas First Steps
course content

Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesChallenge: Creating a SeriesDataFrameQuiz: Creating a SeriesQuiz: Creating a DataFrameAdding a New ColumnInserting a New ColumnDeleting a Row/Column Quiz: Matching the FunctionsWorking with ColumnsQuiz: Extracting Columnsiloc BasicsChallenge: Using iloc
2. Reading Files in Pandas
CSV FilesTXT FilesQuiz: Working with FilesQuiz: Accessing the Columns
3. Analyzing the Data
Viewing the DataQuiz: Using HeadQuiz: Head, Tail, and SampleExploring the DatasetColumn Names and Data TypesFinding Null ValuesQuiz: Identifying Null ValuesChallenge: Dropping Null ValuesChallenge: Filling Null ValuesQuiz: Null ValuesDescribing the Datamax() and min()Quiz: Statistical Operationssum() and count()Unique Values

book
Adding a New Column

We've learned how to create a DataFrame. Now let's explore what we can do with it. First, we'll create a compact DataFrame consisting of 3 columns and 7 rows.


              
1234567

            
import pandas as pd 
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}

countries = pd.DataFrame(countries_data)
print(countries)
copy

You can expand the DataFrame by adding new columns, and the most common way to do it is as follows:

python

  • dataframe is the name of our existing DataFrame to which we'll add new columns;

  • name_of_new_column is the name you're giving to the new column you're adding;

  • value_1, value_2, value_3 are the values that will populate the new column.

Note

The name of the new column should be enclosed in quotation marks and wrapped in square brackets, such as ['NewColumnName']. The values assigned to the new column should also be within square brackets, for example, data['NewColumnName'] = [value1, value2, value3]. If the values are numeric, they can be written without quotes, like [1, 2, 3]. If the values are strings, each one should be enclosed in quotes, like ['A', 'B', 'C'].

Now, we'll add a 'population' column to our pre-existing countries DataFrame.


              
12345678

            
import pandas as pd
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries['population'] = [61399000, 75967000, 39244, 380200, 10380491, 5496000, 2424200]
print(countries)
copy

You can also use dot notation (e.g., df.column) for accessing existing columns, but it cannot be used to create new columns. Always use square brackets (e.g., df['column']) for this purpose.


              
12345678

            
import pandas as pd
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries.population = [61399000, 75967000, 39244, 380200, 10380491, 5496000, 2424200]
print(countries)
copy

As expected, the 'population' column was not created since Pandas doesn't allow columns to be created using this approach.

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 7

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course content

Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesChallenge: Creating a SeriesDataFrameQuiz: Creating a SeriesQuiz: Creating a DataFrameAdding a New ColumnInserting a New ColumnDeleting a Row/Column Quiz: Matching the FunctionsWorking with ColumnsQuiz: Extracting Columnsiloc BasicsChallenge: Using iloc
2. Reading Files in Pandas
CSV FilesTXT FilesQuiz: Working with FilesQuiz: Accessing the Columns
3. Analyzing the Data
Viewing the DataQuiz: Using HeadQuiz: Head, Tail, and SampleExploring the DatasetColumn Names and Data TypesFinding Null ValuesQuiz: Identifying Null ValuesChallenge: Dropping Null ValuesChallenge: Filling Null ValuesQuiz: Null ValuesDescribing the Datamax() and min()Quiz: Statistical Operationssum() and count()Unique Values

book
Adding a New Column

We've learned how to create a DataFrame. Now let's explore what we can do with it. First, we'll create a compact DataFrame consisting of 3 columns and 7 rows.


              
1234567

            
import pandas as pd 
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}

countries = pd.DataFrame(countries_data)
print(countries)
copy

You can expand the DataFrame by adding new columns, and the most common way to do it is as follows:

python

  • dataframe is the name of our existing DataFrame to which we'll add new columns;

  • name_of_new_column is the name you're giving to the new column you're adding;

  • value_1, value_2, value_3 are the values that will populate the new column.

Note

The name of the new column should be enclosed in quotation marks and wrapped in square brackets, such as ['NewColumnName']. The values assigned to the new column should also be within square brackets, for example, data['NewColumnName'] = [value1, value2, value3]. If the values are numeric, they can be written without quotes, like [1, 2, 3]. If the values are strings, each one should be enclosed in quotes, like ['A', 'B', 'C'].

Now, we'll add a 'population' column to our pre-existing countries DataFrame.


              
12345678

            
import pandas as pd
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries['population'] = [61399000, 75967000, 39244, 380200, 10380491, 5496000, 2424200]
print(countries)
copy

You can also use dot notation (e.g., df.column) for accessing existing columns, but it cannot be used to create new columns. Always use square brackets (e.g., df['column']) for this purpose.


              
12345678

            
import pandas as pd
countries_data = {'country' : ['Thailand', 'Philippines', 'Monaco', 'Malta', 'Sweden', 'Paraguay', 'Latvia'],
          'continent' : ['Asia', 'Asia', 'Europe', 'Europe', 'Europe', 'South America', 'Europe'],
          'capital':['Bangkok', 'Manila', 'Monaco', 'Valletta', 'Stockholm', 'Asuncion', 'Riga']}
countries = pd.DataFrame(countries_data)

countries.population = [61399000, 75967000, 39244, 380200, 10380491, 5496000, 2424200]
print(countries)
copy

As expected, the 'population' column was not created since Pandas doesn't allow columns to be created using this approach.

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 7
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