I have a JSON file that I want to convert to CSV. I'm doing it with: import pandas as pd with open('test.json', encoding='utf-8') as inputfile: df = pd.read_json(inputfile) df.to_csv('test.csv', encoding='utf-8', index=False) Everything works but my JSON has structure that I…
I have the pandas latest version installed as of now with python version 3.12.0. While running VS Code, I was running this line of code import pandas as pd data = pd.DataFrame(np.arange(16).reshape((4,4)), index = ['Ohio', 'Colorado', 'Utah', 'New York'], columns…
I am trying to install Pandas in Pycharm using pip (pip install pandas) and I get the following error: Collecting pandas Using cached pandas-2.1.4.tar.gz (4.3 MB) Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend…
I have been asking for help in importing JSON-formatted data from URLs (I am a newbie as far as dealing with JSON) and received a great answer in response to this question. However, I have encountered a complication. Some of…
I am bit rusty in pandas to json transforms. I have a pandas data frame like this for a fictional library: userId visitId readingTime Books BookType u1 1 300 book1,book2,book3 Fiction u2 1 400 book4,book5 Horror u2 2 250 book6…
I tried different ways to get this done, but unable to do so. Please help here. I am trying to explode a nested json list using python pd.json_normalize. After exploding the output is coming in two rows causing duplicate PKeys.…
I have 'n' after > and spaces before <. Is there a way to don't save them when I call to_html method? I have: <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> I want to have: <table border="1" class="dataframe"><thead><tr style="text-align: right;">…
# I'm facing an issue when trying to execute the following code snippet using psycopg2 in place of create_engine. # Code snippet using create_engine (works fine): from sqlalchemy import create_engine import pandas as pd db_url = "your_database_url_here" engine = create_engine(db_url)…
I am using the following bit of code to read the iris dataset from an s3 bucket. import pandas as pd import s3fs s3_path = 's3://h2o-public-test-data/smalldata/iris/iris.csv' s3 = s3fs.S3FileSystem(anon=True) with s3.open(s3_path, 'rb') as f: df = pd.read_csv(f, header = True)…
I have a dataframe which has a structure like below. id, name, value, employee_0_salary, employee_0_skills_0_id, employee_0_skills_1_id, employee_1_salary, employee_1_skills_0_id so on.. Basically formed from a flattened json. But the number of columns exceed 1024. I couldn't copy the structure of json…