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Data Handling for Analysis with Python

ByMaki Pa

Data handling is a crucial aspect of business analysis as it involves collecting, organizing, cleaning, preprocessing, analyzing, and interpreting large volumes of data to extract meaningful insights and make informed business decisions. With the increasing availability of data in modern business environments, it is essential to have effective tools and techniques in place for handling and analyzing data efficiently.

See also Part 1 for beginers: Python for Business Analytics – Customer Experience Management (mietwood.com)

Blog Post 2: Data Handling with Python

Python libraries like Pandas provide powerful data structures and easy-to-use functions for data gatering, cleaning, preprocessing, manipulation, and transformation. These features allow business analysts to efficiently handle missing values, remove outliers, perform aggregations, merge datasets, and more, ensuring the data is in a suitable format for analysis.

The easiest way to get some date is to make a list. Python ofers several types of list. One of it is just an array called in python a list.

a = [1,2,3,4,56,7,78,88,99]

You can save it easaly to file as you can see here. We can save in one line (row) or in many lines. Some preprocessin is already done. Any data you can save and read from file in similar way. But for analyst some other methods are very importatn as reading data from csv, xlsx, from databases and from internet data streems. In this subject Pandas library makes live a lot easier.

Reading Data from CSV Files: Python’s Pandas library offers a simple yet powerful method, read_csv(), to read data from CSV files. Using this function, business analysts can easily import structured tabular data from CSV files into a Pandas DataFrame. The function provides several parameters to customize the import process, such as specifying the delimiter, handling missing values, and defining column names. Once the data is read into a DataFrame, analysts can perform further data manipulation and analysis tasks. Let’s create csv-type file:

column_str = ','.join(column_names)
with open('xx.txt', 'w') as f:
    f.write(column_str+'\n')

with open('xx.txt', 'a') as f:
  for i in range(5):
    a2_str = ','.join([str(random.randint(1,200)) for x in column_names])
    f.writelines(a2_str+'\n')

Now, let’s read this file with help of Pandas.

pd.read_csv('xx.txt')

Running this code we have got dataframe. What is data frame you can read in the internet but imagine it as a excel type table. You can read data also from other sources.

  1. Reading Data from Excel Files: Pandas also provides the read_excel() function to read data from Excel files, making it effortless for business analysts to extract information from spreadsheets. This function allows analysts to specify the sheet name, range of cells, and desired columns. It supports both .xls and .xlsx file formats, providing compatibility with various versions of Excel. The imported data is stored in a DataFrame, enabling analysts to perform data cleaning, transformation, and analysis operations.
  2. Reading Data from MS SQL Server Database: Python’s Pandas library incorporates the read_sql() function, which enables business analysts to extract data from MS SQL Server databases directly into a DataFrame. By establishing a connection to the database using third-party libraries like pyodbc or sqlalchemy, analysts can execute an SQL query and retrieve the results into a Pandas DataFrame. This functionality empowers analysts to seamlessly integrate their data analysis workflow with their database systems, allowing for efficient data retrieval and analysis.
  3. Reading Data from JSON Files: In addition to CSV and Excel files, Pandas provides the read_json() function to read data from JSON files. This function allows business analysts to import structured or semi-structured data stored in JSON format and convert it into a DataFrame for analysis. The read_json() function supports various JSON file types, including single-line or multi-line JSON, nested JSON structures, and JSON arrays. Analysts can customize the import process by specifying parameters like orient, lines, and precise data locations within the JSON file, providing flexibility in handling different JSON datasets.

By leveraging Pandas’ diverse range of functions, business analysts can effortlessly import data from diverse sources, including CSV files, Excel files, MS SQL Server databases, and JSON files, into Pandas DataFrames. This versatility enables analysts to work seamlessly with different data formats, providing a unified and efficient approach to data handling in Python for business analysis.

Introduction to Data Handling in Python

  1. Python Libraries for Data Handling
  • Discuss popular Python libraries used for data handling in business analysis, such as NumPy, Pandas, and matplotlib.
  • Provide an overview of their key features and functionalities.
  1. Data Cleaning and Preprocessing
  • Explain the importance of data cleaning and preprocessing for accurate analysis.
  • Illustrate how Python can be used to clean and preprocess data, including techniques like handling missing values, removing outliers, and standardizing data.
  1. Data Visualization
  • Discuss the significance of data visualization in business analysis and decision-making.
  • Demonstrate how Python libraries like matplotlib and seaborn can be utilized to create informative and visually appealing graphs and charts.
  1. Exploratory Data Analysis (EDA)
  • Explain the concept of EDA and its role in understanding the data.
  • Show examples of using Python to perform EDA, including techniques like descriptive statistics, frequency analysis, and correlation analysis.
  1. Data Manipulation and Transformation
  • Explain various data manipulation and transformation techniques using Python libraries like Pandas.
  • Provide examples of tasks such as filtering, sorting, aggregating, and merging data.
  1. Statistical Analysis
  • Discuss the significance of statistical analysis in business decision-making.
  • Highlight the statistical functions and capabilities offered by Python libraries like NumPy and SciPy.
  1. Data Modeling and Predictive Analytics
  • Explain the use of Python for data modeling and predictive analytics in business analysis.
  • Provide an overview of Python libraries like scikit-learn for tasks such as regression, classification, and clustering.
  1. Conclusion
  • Summarize the key points discussed in the blog post.
  • Emphasize the importance of Python in data handling for business analysis and encourage further exploration and learning.

Remember to organize your content in a logical and coherent manner and provide code examples, screenshots, or diagrams whenever possible to enhance understanding.

Introduction:

  • Highlight the importance of data handling in business analytics.
  • Introduce libraries for data analysis: Pandas and NumPy.

1. Introduction to Pandas:

  • Overview of Pandas data structures: Series and DataFrame.
  • Reading and writing data with Pandas.

2. Data Cleaning and Preprocessing:

  • Dealing with missing values using Pandas.
  • Removing duplicates and outliers.

3. Exploratory Data Analysis (EDA):

  • Basic statistical analysis using Pandas.
  • Creating visualizations with Matplotlib and Seaborn.

4. Introduction to NumPy:

  • Basics of NumPy arrays.
  • Performing mathematical operations on arrays.

5. Data Manipulation with Pandas:

  • Filtering and selecting data.
  • Grouping and aggregating data.

6. Case Study: Analyzing Business Data:

  • Applying the learned concepts to a real-world business dataset.
  • Drawing insights and making basic business recommendations.

Conclusion:

  • Emphasis on the practical application in business analytics.
  • Encouragement to explore more advanced data analysis techniques.

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