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Top Questions People Ask About Pandas, NumPy, Matplotlib & Scikit-learn — Answered!

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 Whether you're a beginner or brushing up on your skills, these are the real-world questions Python learners ask most about key libraries in data science. Let’s dive in! 🐍 🐼 Pandas: Data Manipulation Made Easy 1. How do I handle missing data in a DataFrame? df.fillna( 0 ) # Replace NaNs with 0 df.dropna() # Remove rows with NaNs df.isna(). sum () # Count missing values per column 2. How can I merge or join two DataFrames? pd.merge(df1, df2, on= 'id' , how= 'inner' ) # inner, left, right, outer 3. What is the difference between loc[] and iloc[] ? loc[] uses labels (e.g., column names) iloc[] uses integer positions df.loc[ 0 , 'name' ] # label-based df.iloc[ 0 , 1 ] # index-based 4. How do I group data and perform aggregation? df.groupby( 'category' )[ 'sales' ]. sum () 5. How can I convert a column to datetime format? df[ 'date' ] = pd.to_datetime(df[ 'date' ]) ...
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Python Program: JSON to CSV Conversion

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JavaScript object notion is also called JSON file, it's data you can write to a CSV file. Here's a sample python logic for your ready reference. 




You can write a simple python program by importing the JSON, and CSV packages. This is your first step. It is helpful to use all the JSON methods in your python logic. That means the required package is JSON.

So far, so good. In the next step, I'll show you how to write a Python program. You'll also find each term explained.


What is JSON File

JSON is key value pair file. The popular use of JSON file is to transmit data between heterogeneous applications. Python supports JSON file.


What is CSV File

The CSV is comma separated file. It is popularly used to send and receive data.


How to Write JSON file data to a CSV file

Here the JSON data that has written to CSV file. It's simple method and you can use for CSV file conversion use.

import csv, json

json_string = '[{"value1": 1, "value2": 2,"value3": 1.234}]'
data = json.loads(json_string)
headers = data[0].keys()

with open('sample.csv', 'w') as f:
writer = csv.DictWriter(f, fieldnames=headers)
writer.writeheader()
writer.writerows(data)


with open('sample.csv', 'r') as f:
    print(f)
    for row in f:
        print(row)

Output:

<_io.TextIOWrapper name='file.csv' mode='r' encoding='UTF-8'>
value1,value2,value3

1,2,1.234


** Process exited - Return Code: 0 **
Press Enter to exit terminal

Conclusion

The output CSV file has both headers and rows, and the data is comma seprated.


References

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