What is data analysis using python?
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Our mission: to help people learn to code for free. We accomplish this by creating thousands of videos, articles, and interactive coding lessons - all freely available to the public. We also have thousands of freeCodeCamp study groups around the world. Donations to freeCodeCamp go toward our education initiatives, and help pay for servers, services, and staff. You can make a tax-deductible donation here. Data is getting generated at a massive rate, by the minute. Organizations, on the other hand, are trying to explore every opportunity to make sense of this data. This is where Data analytics has become crucial in running a business successfully. It is commonly used in companies to drive profit and business growth. In this article, we’ll learn
Data analytics using Python. Data analytics is the process of exploring and analyzing large datasets to make predictions
and boost data-driven decision making. Data analytics allows us to collect, clean, and transform data to derive meaningful insights. It helps to answer questions, test hypotheses, or disprove theories. Let’s understand the various applications of data analytics. Data analytics is used in most sectors of businesses. Here are some primary areas where data analytics does its magic: Data analytics can be broadly classified into 3 types: It tells you what has happened. It can be done using an exploratory data analysis. Example: Studying the total units of chairs sold and the profit that was made in the past. It tells you what will happen. It can be achieved by building predictive models. Example: Predicting the total units of chairs that would sell and the profit we can expect in the
future. It tells you how to make something happen. It can be done by deriving key insights and hidden patterns from the data. Example: Finding ways to improve sales and profit of chairs. The graph below represents the difficulty level and values the can be derived from the different types of data analytics. Data Analytics Process StepsThere are primarily five steps involved in the data analytics process, which include:
Why Data Analytics Using Python?There are many programming languages available, but Python is popularly used by statisticians, engineers, and scientists to perform data analytics. Here are some of the reasons why Data Analytics using Python has become popular:
Python Libraries for Data AnalyticsOne of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries. NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform. Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data. Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data. SciPy: SciPy library is used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing. Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models. Now, let’s look at how to perform data analytics using Python and its libraries. Data Analytics Using the Python Library, NumPyLet’s see how you can perform numerical analysis and data manipulation using the NumPy library. 1. Create a NumPy array. 2. Access and manipulate elements in the array. 3. Create a 2-dimensional array and check the shape of the array. 4. Access elements from the 2D array using index positions. 5. Create an array of type string. 6. Using the arange() and linspace() function to evenly space values in a specified interval. 7. Create an array of random values between 0 and 1 in a given shape.
8. Create an array of constant values in a given shape. 9. Repeat each element of an array by a specified number of times using repeat() and tile() functions. 10. Create an identity matrix using eye() and identity() function. 11. Create a 5x5 2D array for random numbers between 0 and 1.
12. Sum an array along the column. 13. Sum an array along the row.
14. Calculate the mean, median, standard deviation, and variance. 15. Sort an array along the row using the sort() function. 16. Append elements to an array using the append() function. 17. Delete multiple elements in an array. 18. Concatenate elements from 2 arrays. Get broad exposure to key technologies and skills used in data analytics and data science, including statistics with the Post Graduate Program in Data Analytics. Data Analytics Using Python Libraries, Pandas and MatplotlibWe’ll use a car.csv dataset and perform exploratory data analysis using Pandas and Matplotlib library functions to manipulate and visualize the data and find insights. 1. Import the libraries. 2. Load the dataset using pandas read_csv() function. 3. Display the head of the dataset using the head() function. 4. Display the bottom 5 rows from the dataset using the tail() function. 5. Print summary statistics of the dataset using the describe() function. 6.Plot a histogram for all the variables. 7. Box plot to visualize the relationship between vehicle size and engine hp. 8. Build a pair plot using the seaborn library. 9. Drop irrelevant columns from the dataset using drop() function. 10. Use rename() function to rename the columns. 11. Print the total number of duplicate rows. 12. Remove the duplicate rows using the drop_duplicates() function. 13. Drop the missing values from the dataset. 14. Plot a histogram to find the number of cars per brand. 15. Draw a correlation plot between the variables. ConclusionData is getting generated rapidly in various formats. And companies are relying on data analytics to derive valuable information and hidden insights from this data. After reading this ‘Data analytics using Python’ article, you would have learned what data analytics is and the various applications of data analytics. You also looked at the different types of data analytics and process steps. Finally, you performed data analytics using Python’s NumPy, Pandas, and Matplotlib libraries. Do you have any questions for us on this ‘Data analytics using Python’ article? If so, then please put it in the comments section of this article. Our team of experts will help you solve your queries at the earliest. What is data analysis in programming?Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
Where is Python data analysis used?Python for Data Analysis. See Sisense in action:. One of the most common uses for Python is in its ability to create and manage data structures quickly — Pandas, for instance, offers a plethora of tools to manipulate, analyze, and even represent data structures and complex datasets.. Can you use Python to analyze data?As we have mentioned, Python works well on every stage of data analysis. It is the Python libraries that were designed for data science that are so helpful. Data mining, data processing, and modeling along with data visualization are the 3 most popular ways of how Python is being used for data analysis.
What is meant by data analysis?Data Analysis. Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data.
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