PYTHON
PYTHON
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Python is a high-level, versatile programming language known for its simplicity and readability. Python emphasizes code readability with its use of significant whitespace and clean syntax. It's widely used in various domains, such as web development, data analysis, artificial intelligence, scientific computing, and more.
Python is a high-level, versatile programming language known for its simplicity and readability. Python emphasizes code readability with its use of significant whitespace and clean syntax. It's widely used in various domains, such as web development, data analysis, artificial intelligence, scientific computing, and more.
Mounting Google Drive: The first code cell mounts Google Drive to the Colab environment. This allows access to files stored in Google Drive. The output indicates successful mounting.
Mounting Google Drive: The first code cell mounts Google Drive to the Colab environment. This allows access to files stored in Google Drive. The output indicates successful mounting.
Loading Libraries and Dataset: The second code cell imports necessary libraries (pandas, matplotlib.pyplot, and seaborn) and loads a dataset from a CSV file stored in Google Drive.
Loading Libraries and Dataset: The second code cell imports necessary libraries (pandas, matplotlib.pyplot, and seaborn) and loads a dataset from a CSV file stored in Google Drive.
Data Frame Output: The dataset contains information on GDP (nominal) per capita from different organizations and years. Columns include country names, regions, and GDP estimates from the IMF, World Bank, and UN.
Data Frame Output: The dataset contains information on GDP (nominal) per capita from different organizations and years. Columns include country names, regions, and GDP estimates from the IMF, World Bank, and UN.
The image displays two outputs from a Python script using the pandas library to show the first 10 and last 5 rows of a DataFrame named df. This DataFrame includes data about various countries/territories, their regions, and GDP estimates from different organizations.
The image displays two outputs from a Python script using the pandas library to show the first 10 and last 5 rows of a DataFrame named df. This DataFrame includes data about various countries/territories, their regions, and GDP estimates from different organizations.
Each row provides GDP estimates and the corresponding years from the IMF, World Bank, and UN for each country/territory.
Each row provides GDP estimates and the corresponding years from the IMF, World Bank, and UN for each country/territory.
The image shows a Python code output displaying histograms of a DataFrame using the df.hist(figsize=(15, 15)) function.
The image shows a Python code output displaying histograms of a DataFrame using the df.hist(figsize=(15, 15)) function.
Each histogram shows the frequency distribution of the respective dataset values. The IMF_Year and WorldBank_Year histograms have a significant number of entries around the year 2000. On the other hand, the WorldBank_Estimate and UN_Estimate histograms show a right-skewed distribution with most values concentrated at the lower end and a few extending to higher ranges.
Each histogram shows the frequency distribution of the respective dataset values. The IMF_Year and WorldBank_Year histograms have a significant number of entries around the year 2000. On the other hand, the WorldBank_Estimate and UN_Estimate histograms show a right-skewed distribution with most values concentrated at the lower end and a few extending to higher ranges.
This plot visually represents the number of students in each class, with clear labels and a pastel color scheme.
This plot visually represents the number of students in each class, with clear labels and a pastel color scheme.
The image shows a Python code snippet and its output, which is a box plot visualizing the distribution of marks by class.
The image shows a Python code snippet and its output, which is a box plot visualizing the distribution of marks by class.
The code uses the Seaborn and Matplotlib libraries to create a bar plot that visualizes the number of countries by UN region, excluding the "World" category.
The code uses the Seaborn and Matplotlib libraries to create a bar plot that visualizes the number of countries by UN region, excluding the "World" category.
The image shows a Python script and its output, which is a heatmap visualizing the correlation matrix for numeric columns in a dataset.
The image shows a Python script and its output, which is a heatmap visualizing the correlation matrix for numeric columns in a dataset.
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