10Dec, 2024
    The difference between Data Analyst and Business Analyst Hindi

    Data Analyst और Business Analyst के बीच अंतर

    Data Analyst और Business Analyst की भूमिकाओं और कार्यों के बीच महत्वपूर्ण अंतर।

    Role (भूमिका)

    Data Analyst: Data का analysis करना और रिपोर्ट तैयार करना।

    Business Analyst: Business की समस्याओं का हल ढूँढना और strategies बनाना।

    Skills (कौशल)

    • Data Analyst: SQL, Excel, Python, R, Tableau
    • Business Analyst: Business processes, financial analysis, Power BI

    Main Objective (मुख्य उद्देश्य)

    Data Analyst: Data से insights निकालना और उन्हें समझना।

    Business Analyst: Business processes को improve करना और strategies बनाना।

    Work Process (कार्य प्रक्रिया)

    • Data Analyst: Data collection, cleaning, analysis और reporting
    • Business Analyst: Problems का analysis करना और solutions/plan बनाना

    Data Sources (डेटा स्रोत)

    • Data Analyst: Structured data
    • Business Analyst: Business processes, reports, और user data

    Reporting (रिपोर्टिंग)

    • Data Analyst: Data reports और visualizations
    • Business Analyst: Business reports और strategic presentations

    Requirements (आवश्यकताएँ)

    • Data Analyst: Technical knowledge की जरूरत
    • Business Analyst: Business understanding और communication skills की जरूरत

    Skills (कौशल)

    Data Analyst और Business Analyst के बीच कौशल में अंतर जानें।

    Data Analyst के कौशल

    • SQL – Structured Query Language, जो database को manage करने के लिए उपयोग किया जाता है।
    • Excel – Data analysis और visualization के लिए सबसे ज्यादा इस्तेमाल होने वाला spreadsheet software।
    • Python – Data analysis और automation के लिए उपयोग की जाने वाली एक powerful programming language।
    • R – Statistical computing और graphics के लिए इस्तेमाल होने वाली एक programming language।
    • Tableau – Data visualization tool, जिससे interactive और shareable dashboards बनाए जाते हैं।

    Business Analyst के कौशल

    • Business Processes – Business workflows और processes को समझना और optimize करना।
    • Financial Analysis – Business decisions को support करने के लिए financial data का analysis करना।
    • Power BI – Business analytics tool, जो data से insights निकालने और visualizing करने के लिए उपयोग किया जाता है।

    Main Objective (मुख्य उद्देश्य)

    Data Analyst और Business Analyst के मुख्य उद्देश्य में अंतर।

    Data Analyst का मुख्य उद्देश्य

    Data Analyst: Data से insights निकालना और उन्हें समझना ताकि business decisions को improve किया जा सके।

    Business Analyst का मुख्य उद्देश्य

    Business Analyst: Business processes को improve करना और strategies बनाना ताकि efficiency और growth को maximize किया जा सके।

    Work Process (कार्य प्रक्रिया)

    Data Analyst और Business Analyst की कार्य प्रक्रिया में अंतर।

    Data Analyst की कार्य प्रक्रिया

    • Data Collection: Data का इकट्ठा करना विभिन्न sources से।
    • Data Cleaning: Data में से अनावश्यक या गलत जानकारी को हटाना।
    • Data Analysis: Data का विश्लेषण करके trends और patterns को पहचानना।
    • Reporting: Analysis का रिपोर्ट तैयार करना और business stakeholders को पेश करना।

    Business Analyst की कार्य प्रक्रिया

    • Problem Analysis: Business समस्याओं का गहराई से विश्लेषण करना।
    • Solution Planning: समस्याओं के समाधान के लिए रणनीतियाँ और योजनाएँ बनाना।
    • Implementation Support: योजनाओं को लागू करने के लिए टीम को समर्थन देना।
    • Monitoring: कार्यप्रणाली के परिणामों को ट्रैक करना और आवश्यकता अनुसार सुधार करना।

    Data Sources (डेटा स्रोत)

    Data Analyst और Business Analyst के लिए डेटा स्रोत में अंतर।

    Data Analyst के डेटा स्रोत

    • Structured Data: साफ और संगठित डेटा, जो आसानी से tables और databases में रखा जाता है।

    Business Analyst के डेटा स्रोत

    • Business Processes: कार्यप्रणालियों से संबंधित डेटा, जैसे operations और workflows।
    • Reports: Reports और historical data, जो business performance को दर्शाती हैं।
    • User Data: Customers या users के द्वारा प्रदान किया गया डेटा, जो उनके व्यवहार और preferences को दर्शाता है।

    Reporting (रिपोर्टिंग)

    Data Analyst और Business Analyst के रिपोर्टिंग कार्य में अंतर।

    Data Analyst की रिपोर्टिंग

    • Data Reports: Data से संबंधित रिपोर्ट्स तैयार करना, जो insights और trends को दर्शाती हैं।
    • Visualizations: Data visualization tools का उपयोग करके charts और graphs के रूप में रिपोर्ट्स को प्रस्तुत करना।

    Business Analyst की रिपोर्टिंग

    • Business Reports: Business के performance और progress को दर्शाने वाले reports तैयार करना।
    • Strategic Presentations: Business के future goals और strategies पर presentations तैयार करना, जो stakeholders को जानकारी देती हैं।

    Requirements (आवश्यकताएँ)

    Data Analyst और Business Analyst की आवश्यकताएँ अलग-अलग होती हैं।

    Data Analyst की आवश्यकताएँ

    • Technical Knowledge: Data analysis और programming languages (जैसे SQL, Python, R) में तकनीकी ज्ञान।
    • Data Tools: Data visualization और analysis tools का अनुभव, जैसे Tableau और Excel।
    • Analytical Skills: Data में patterns और insights निकालने के लिए मजबूत विश्लेषणात्मक कौशल।

    Business Analyst की आवश्यकताएँ

    • Business Understanding: Business processes, industry trends, और business models की गहरी समझ।
    • Communication Skills: Business stakeholders के साथ प्रभावी संवाद करने की क्षमता।
    • Problem-Solving: Business समस्याओं का समाधान ढूँढने की क्षमता और रणनीतियाँ विकसित करना।

    Explore Our Helpful Guides

    9Nov, 2023
    A Guide to Insert, Update, and Delete Rows in Python Pandas Data Analytics
    Python: A Deep Dive into Data Structures for Efficient Programming

    In the ever-evolving realm of data science, the ability to manipulate and mold data is a fundamental skill. Whether you’re an aspiring data scientist or a seasoned analyst, proficiency in tools like Pandas can make all the difference. Among its many capabilities, Pandas excels at handling tabular data, providing a robust foundation for data manipulation in Python.

    This blog aims to delve into the intricacies of efficient data manipulation in Pandas, with a specific focus on inserting, updating, and deleting rows. These operations are the building blocks of data transformation, and mastering them opens the door to a world of possibilities in data analysis and exploration.

    So, fasten your seatbelts as we embark on a journey to unravel the secrets of Pandas, exploring techniques that will empower you to wield this powerful tool with finesse. Whether you’re looking to add new insights to your data, update existing records, or clean up unnecessary information, this guide has you covered. Let’s dive into the heart of Pandas and unlock the potential of data manipulation in Python.

     

    import and read in Pandas

    import pandas as pd
df=pd.read_excel('E:\JOINS\CUSTOMERDATA.xlsx')
print(df)
    • import pandas as pd
      This line imports the Pandas library and aliases it as pd for brevity. The alias is a common convention in the data science community.
    • Reading Excel File:
      df = pd.read_excel(‘E:\JOINS\CUSTOMERDATA.xlsx’)
      Here, you use the pd.read_excel() function to read the contents of an Excel file into a Pandas DataFrame (df). The file path ‘E:\JOINS\CUSTOMERDATA.xlsx’ points to the location of your Excel file.
    • If your Excel file has multiple sheets, you can specify the sheet name using the sheet_name parameter (e.g., pd.read_excel(‘file.xlsx’, sheet_name=’Sheet1′)).
    • Displaying the DataFrame:
    • print(df)
      This line prints the entire DataFrame to the console. This is useful for quickly inspecting the structure and contents of your data.

    Note: If your DataFrame is too large, printing it in its entirety may not be practical. In such cases, you can use df.head() to display the first few rows or specify the number of rows to display.

    In summary, your code reads an Excel file into a Pandas DataFrame and then prints the DataFrame to the console, allowing you to inspect the data. If you have any specific questions about the DataFrame or if you’d like to perform certain operations on it, feel free to let me know!

    Basic Operator in Pandas

    # Displaying the current DataFrame
print(df)

# Selecting Columns
names = df['customer_name']
print("\nSelected 'customer_name' column:\n", names)

# Filtering Data
young_customers = df[df['customer_id'] < 104]
print("\nCustomers with 'customer_id' less than 104:\n", young_customers)

# Sorting Data
sorted_df = df.sort_values(by='customer_name', ascending=False)
print("\nDataFrame sorted by 'customer_name' in descending order:\n", sorted_df)

    1.Displaying the DataFrame:

    print(df) This prints the entire DataFrame, showing all the rows and columns.

    2.Selecting Columns:

    names = df[‘customer_name’] print(“\nSelected ‘customer_name’ column:\n”, names) It extracts the ‘customer_name’ column from the DataFrame and prints it. names is now a Series containing only the ‘customer_name’ column.

    3.Filtering Data:

    young_customers = df[df[‘customer_id’] < 104] print(“\nCustomers with ‘customer_id’ less than 104:\n”, young_customers) It filters the DataFrame to include only the rows where the ‘customer_id’ is less than 104.

    4. Sorting Data:

    sorted_df = df.sort_values(by=’customer_name’, ascending=False) print(“\nDataFrame sorted by ‘customer_name’ in descending order:\n”, sorted_df) It sorts the DataFrame based on the ‘customer_name’ column in descending order and then prints the sorted DataFrame. Seems like you’re manipulating and exploring your data. Anything specific you’re trying to achieve with this DataFrame? OUTPUT 
     customer_id customer_name       customer_email
0          101         Alice    alice@example.com
1          102           Bob      bob@example.com
2          103       Charlie  charlie@example.com
3          104         David    david@example.com
4          105           Eve      eve@example.com
5          106        Friday        Fri@fmail.com

Selected 'customer_name' column:
 0      Alice
1        Bob
2    Charlie
3      David
4        Eve
5     Friday
Name: customer_name, dtype: object

Customers with 'customer_id' less than 104:
    customer_id customer_name       customer_email
0          101         Alice    alice@example.com
1          102           Bob      bob@example.com
2          103       Charlie  charlie@example.com

DataFrame sorted by 'customer_name' in descending order:
    customer_id customer_name       customer_email
5          106        Friday        Fri@fmail.com
4          105           Eve      eve@example.com
3          104         David    david@example.com
2          103       Charlie  charlie@example.com
1          102           Bob      bob@example.com
0          101         Alice    alice@example.com

    Adding a new row using loc with label-based indexing

    new_data_loc = {'customer_id': 110.0, 'customer_name': 'Grace', 'customer_email': 'grace@example.com'}
df.loc[len(df)] = new_data_loc

    new_data_loc

    is a dictionary that contains information about a new customer. It has keys such as ‘customer_id’, ‘customer_name’, and ‘customer_email’, along with their respective values.

    df.loc[len(df)]

    is selecting a specific location within the DataFrame df. len(df) returns the length of the DataFrame, which corresponds to the index where the new row will be added.

    df.loc[len(df)] =

    new_data_loc assigns the values in the new_data_loc dictionary to the row in the DataFrame at the index position obtained by len(df). This effectively appends a new row to the DataFrame df with the information provided in the new_data_loc dictionary.

    So, the code snippet is taking the dictionary new_data_loc and using it to add a new row to the DataFrame df with the information of a customer named Grace.

    Adding a new row using iloc with integer-based indexing

    new_data_iloc = {'customer_id': 111.0, 'customer_name': 'Harry', 'customer_email': 'harry@example.com'}
df.loc[df.index.max() + 1] = new_data_iloc

# Displaying the updated DataFrame
print(df)

    This code is similar to the previous one but uses df.index.max() + 1 to find the next available index for the new row. Here’s a breakdown:

    new_data_iloc

     

    is a dictionary containing details about a new customer with keys ‘customer_id’, ‘customer_name’, and ‘customer_email’, each with their respective values.

    df.loc[df.index.max() + 1]

    locates the row in the DataFrame df at the next available index. df.index.max() returns the maximum index value in the DataFrame, and adding 1 ensures that the new row is added to the next index.

    df.loc[df.index.max() + 1]

    = new_data_iloc assigns the values from the new_data_iloc dictionary to the row in the DataFrame df at the index position determined by df.index.max() + 1. This effectively appends a new row to the DataFrame df with the information provided in the new_data_iloc dictionary.

    The print(df) statement displays the updated DataFrame with the new row added, showing the customer named Harry with the given details.

    This code is adding a new row to the DataFrame df with the information of a customer named Harry. The printed output will show the updated DataFrame with this new addition.

    Delete row in data frame

    df=df[df['customer_id']!=106]
print(df)

    This line of code performs a filtering operation on the DataFrame df using a condition to remove rows where the ‘customer_id’ column is equal to 106. Let’s break it down:

    df[‘customer_id’] != 106

    creates a boolean Series where each row is evaluated to True or False based on whether the ‘customer_id’ for that row is not equal to 106.

    df[df[‘customer_id’] != 106]

    filters the DataFrame df by selecting only those rows where the condition df[‘customer_id’] != 106 is True. This effectively removes the rows where the ‘customer_id’ is equal to 106 from the DataFrame.

    The updated DataFrame is reassigned to the variable df. Now, df contains all the rows where the ‘customer_id’ is not equal to 106.

    The result displayed by print(df) will show the DataFrame after removing all rows where the ‘customer_id’ is equal to 106. It filters out those specific rows, leaving only the rows where the ‘customer_id’ is different from 106.

    Update row in data frame

    df.loc[df['customer_id']==104,'customer_name']='Ramesh'
print(df)

    This line of code modifies the ‘customer_name’ in the DataFrame df for the rows where the ‘customer_id’

    is equal to 104, setting their ‘customer_name’ to ‘Ramesh’. Let’s break it down:

    df[‘customer_id’] == 104

    creates a boolean Series that checks each row in the DataFrame to identify where the ‘customer_id’ column is equal to 104.

    df.loc[df[‘customer_id’] == 104, ‘customer_name’] = ‘Ramesh’ uses the .loc function to locate rows meeting the condition df[‘customer_id’] == 104. It specifically targets the ‘customer_name’ column and sets the value to ‘Ramesh’ for those rows.

    So, this code line updates the ‘customer_name’ to ‘Ramesh’ for all rows in the DataFrame df where the ‘customer_id’ is equal to 104. This operation effectively changes the ‘customer_name’ selectively for rows that meet the specified condition. The printed output via print(df) will display the DataFrame after this modification.

    Inserting a New Column

    ages = [25, 30, 28, 33, 27,22,21]

# Inserting 'age' column into the DataFrame
df['age'] = ages

    To insert a new column ‘age’ in the DataFrame:


     Assuming ‘age’ data for each customer
    ages = [25, 30, 28, 33, 27]

     Inserting ‘age’ column into the DataFrame


    df[‘age’] = ages

    1. The code uses this line to add the ‘age’ column to the DataFrame df and populates it with the values from the ages list.

      • This operation aligns the ages in the list with the rows of the DataFrame based on their indices. The length of the list should match the number of rows in the DataFrame.

      • Each age value in the list corresponds to a row in the DataFrame, with the first age (25) being assigned to the first row, the second age (30) to the second row, and so on.

    By executing this code, the ‘age’ column will be added to your DataFrame, providing age information aligned with the existing rows of your data.

    Deleting a Column:

    Deleting columns in a DataFrame is a common operation when you’re working with data in pandas, a popular data manipulation library in Python. You can delete one or more columns using various methods. Here’s how you can do it:

    1. Using drop Method
    The drop method is versatile and can be used to delete both rows and columns by specifying the axis parameter.

    Delete a single column:

    import pandas as pd

    # Sample DataFrame
    df = pd.DataFrame({
    ‘A’: [1, 2, 3],
    ‘B’: [4, 5, 6],
    ‘C’: [7, 8, 9]
    })

    # Delete column ‘B’
    df = df.drop(‘B’, axis=1)
    print(df)

    2. Using del Keyword

    The del keyword is a Python statement that removes an item by its name. It’s straightforward for removing a single column but cannot directly handle multiple columns without a loop or additional logic.

     

    # Delete column 'B'
del df['B']
print(df)

    Using pop Method

    The pop method removes a column and returns it as a series. This can be useful if you want to use the removed column for something else.

    # Remove and return column 'B'
removed_column = df.pop('B')
print(df)
print(removed_column)

    Selecting Columns to Keep


    Instead of deleting unwanted columns, you can select the columns you want to keep. This is not a direct deletion method but effectively achieves the same result.

    # Keep only columns 'A' and 'C'
    df = df[['A', 'C']]
    print(df)

    Frequently Asked Questions: Python Pandas Data Analytics

    1. What is Pandas, and why is it used?

    Pandas is a Python library for data manipulation and analysis. It provides structures like DataFrame and Series to handle data efficiently, making tasks like cleaning, transforming, and visualizing data easier.

    2. How can I insert a new row into a Pandas DataFrame?

    To insert a row, use DataFrame.loc or append(). Example:

    import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df.loc[2] = [5, 6]  # Insert at index 2
print(df)

    3. How do I update specific rows in a DataFrame?

    Use .loc to update rows. Example:

    df.loc[1, 'A'] = 10  # Update row 1, column 'A'
df.loc[df['B'] == 3, 'A'] = 15  # Update rows conditionally

    4. How do I delete rows by index in a DataFrame?

    Use the drop() method:

    df = df.drop(index=1)  # Deletes the row with index 1

    5. Can I delete rows based on a condition?

    Yes, filter the DataFrame using a condition. Example:

    df = df[df['A'] != 10]  # Keep rows where 'A' is not 10

    6. What is the difference between loc and iloc in Pandas?

    loc is label-based indexing (e.g., row/column names), while iloc is integer-based indexing (e.g., row/column positions).

    Example:

    df.loc[1, 'A']  # Access row with index 1, column 'A'
df.iloc[1, 0]   # Access the 2nd row, 1st column

    7. How do I add multiple rows to a DataFrame?

    Use append() with a list of dictionaries:

    new_rows = [{'A': 7, 'B': 8}, {'A': 9, 'B': 10}]
df = df.append(new_rows, ignore_index=True)
print(df)

    8. How can I reset the index of a DataFrame?

    Use reset_index(). Example:

    df = df.reset_index(drop=True)  # Resets and removes old index

    9. Can I use Pandas to handle missing data?

    Yes, Pandas offers methods like fillna() to replace missing values and dropna() to remove rows/columns with missing values.

    Example:

    df = df.fillna(0)  # Replace missing values with 0
df = df.dropna()   # Drop rows with missing values

    10. How do I save a modified DataFrame to a file?

    Use to_csv(), to_excel(), or similar methods:

    df.to_csv('output.csv', index=False)  # Save to CSV
df.to_excel('output.xlsx', index=False)  # Save to Excel
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