In this short guide, you’ll see how to create an executable of a Python script using cx_Freeze. Here are the full steps to create an executable on Windows. Steps to Create an Executable using cx_Freeze Step 1: Add Python to Windows Path If you haven’t already done so, add Python to Windows path. You can … Read more

In this short guide, you’ll see how to check the PIP version on Windows. The general command to check the PIP version on Windows is: Copy pip –version Note that if you already added Python to Windows Path, then simply open the Command Prompt and type the above command. Otherwise, check the full steps below. … Read more

To count the number of keys in a dictionary in Python: Copy my_dict = {“key_1”: 1, “key_2”: 2, “key_3”: 3, “key_4”: 4, “key_5”: 5} count_keys = len(my_dict) print(count_keys) Here, the result is 5 keys. Count the number of keys in a nested dictionary The following function can be used to count the number of keys … Read more

To convert Excel file to a JSON file using Python: Copy import pandas as pd # Load Excel to DataFrame path_excel = ‘path_to_excel_file.xlsx’ df = pd.read_excel(path_excel, engine=’openpyxl’) # Convert DataFrame to JSON json_data = df.to_json(orient=’records’, indent=4) print(json_data) # Write JSON data to file path_json = ‘final_result.json’ with open(path_json, ‘w’) as json_file: json_file.write(json_data) In case needed, … Read more

To convert JSON data to Excel using Python: Copy # Json to DataFrame df = pd.json_normalize(json_data) # DataFrame to Excel excel_filename = ‘json_data_to_excel.xlsx’ df.to_excel(excel_filename, index=False) In case needed, install the openpyxl package using pip install openpyxl. Example 1 of converting JSON to Excel using Python The Python script: Copy import pandas as pd json_data = … Read more

Here are different ways to filter rows in Pandas DataFrame using RegEx: (1) Get all the rows where the name starts with ‘B’: Copy import pandas as pd data = {‘name’: [‘Bill’, ‘Maria’, ‘David’, ‘April’, ‘Bob’], ‘age’: [28, 42, 33, 57, 25], ‘country’: [‘Brazil’, ‘Mexico’, ‘Cambodia’, ‘Bolivia’, ‘India’] } df = pd.DataFrame(data) pattern = r’^B\w*’ … Read more