Summary  
The chapter shows how to expand a pandas DataFrame by adding new columns using the `df['new_column'] = […]` syntax and explains that dot notation can access but cannot create columns.

General domain of usage  
Data analysis

You have learned how to create a `DataFrame`. Next, explore what you can do with it. First, create a compact `DataFrame` with **3** columns and **7** rows.


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)

You can expand the `DataFrame` by adding new columns using the following syntax:

```python
dataframe['name_of_new_column'] = [value_1, value_2, value_3]
```

* `dataframe` is the existing `DataFrame` you are adding a column to;
* `name_of_new_column` is the name of the new column;
* `value_1, value_2, value_3` are the values that fill the new column.


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']`.

Note

Next, add a `'population'` column to the existing `countries` DataFrame.

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)

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.

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)

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

Pandas is an extremely user-friendly library for data analysis. It's also designed to handle large datasets, using data structures like DataFrame and Series. This makes it an invaluable tool for Data Science. In this guide, you'll get acquainted with a range of statistical functions, including how to find correlations, modes, medians, and maximum and minimum values within a dataset. You'll also learn how to handle missing values and manipulate specific values, as well as how to remove them.

Dive into the fundamentals of the pandas library. Learn to create and manipulate Series and DataFrames, explore their structure, and build confidence in performing essential data operations.

Master how to load and manage data from CSV and TXT files. Gain practical experience importing datasets and preparing them for further analysis using pandas tools.

Learn to explore, clean, and summarize data effectively. Understand how to handle missing values, extract insights, and perform basic statistical operations with pandas.

Adding a New Column