I want to extract a snow depth value from a weather site to a dataframe. (https://www.yr.no/nb/sn%C3%B8dybder/NO-46/Norge/Vestland) Specifically the snow depth for the Jordalen - Nåsen area. Screen shot The closest I've gotten is printing all the values using this code:…
i have the following groupby results as a dataframe called grouped: long_url counts 3 reddit.com 542 2 linkedin.com 529 4 twitter.com 525 1 google.com 502 0 github.com 476 5 youtube.com 469 i would like to turn the results into a…
I am trying to use the result of data frame that I generated from Azure Blob Storage and apply to the next step (where it extracts data in certain way). I have tested both sides (generating data from Azure Blob…
Here is my code: with open(r'unique_columns.json', 'r') as f: config = json.load(f) unique_col_comb = config['Unique_Column_Combination']['TABLE_NAME'] df = pd.read_csv(f's3://path/to/file.csv', sep='|') df_unique = df.set_index([unique_col_comb]).index.is_unique print(df_unique) My JSON looks like this: { "Unique_Column_Combination": { "TABLE_NAME": "COL1, COL2, COL3" } } I get the…
I am struggling with creating a pandas based html table that will look like the one in the attached picture. As you can see there are multiple table headers, colspans, rowspans, shadings, etc. In more detail, I am developing a…
I want to read multiple text file from s3 bucket which will make up a data frame of 10M records and 900 columns. But it is taking too long to read data using ml.p3.2xlarge instance. Moreover, I would like to…
creating a resultant DataFrame that combines information from both sources while filling missing values in 'Product Strings' with corresponding values from 'df2'. import pandas as pd # DataFrame 1 data1 = {'Company ID': [1, 2, 3], 'Product ID': ['D1', 'D2',…
This site page has text bubbles appearing when hovering over values in columns "Score" and "XP LVL". It appears that read_html will concatenate cell content and text bubble. Splitting those in post-processing is not always obvious and I seek a…
I need to find unique records from mysql table / dataframe. As per the business logic, if the time difference is less than 5 seconds (mobile number and code should be the same) then that record is considered as a…
I am on intel dev cloud and using Intel OneAPI. This is my code till now: # first block of jupyter notebook import modin.pandas as pd # second block of jupyter notebook df = pd.read_csv('dataset/dataset.csv') df.head() # output of second…