Say I have a dataframe ‘df’ that contains a list of files and their contents: How can I reorder this df if I have ordered lists of how the ‘Field’ column should be ordered? So that the resulting df is re ordered like so (I am not trying to just sort ‘Field’ in reverse alphabetical order, this example is just
Tag: dataframe
Spline interpolation on dataframes by row
I have the following data frame: I am trying to apply a spline interpolation on each row to get the values for 2017 and 2018 using the following code: However, I get the following error: ValueError: Index column must be numeric or datetime type when using spline method other than linear. Try setting a numeric or datetime index column before
Add hours to datetime of rows in a specific time interval
Let it be the following Python Panda DataFrame: Given a date and time range (start and end) and a country_ID, I want to add 2 hours to the rows that are in that range: Example: Answer Try your logic with boolean indexing (date must also be a datetime object, not a string):
Change pandas dataframe content in a function
I’m writing a class that does one hot encoding, but it doesn’t work as I expected. On my main code I have this: The class method is the following: Now, with print(data.columns) I can see that the method works correctly, but when train_x_categorical.head() runs I can’t see the effect of the method applyOneHotEncoding. I don’t understand why this is happening
Using DataFrame cross join throw no common columns to perform merge on
I’d like to create a third column as a result of a cross join between my Columns A and B: They have the following unique values: I’d like to have a df[‘C’] with the combination of all cross joins, thus we should have 6 * 4 = 24 unique values in it: Thus we should have the following: Using this
how to count data in a certain column in python(pandas)?
hope you’re doing well . i tried counting green color row after another green colored row in the table below In [1]: df = pd.DataFrame([[green], [red], [red]], columns=[‘A’]) the code i tried to count greengreen: but it didn’t work,hope you can help. note: i’m new to data science Answer You can use: As a one-liner (python ≥ 3.8): example input:
Remove rows in pandas dataframe if any of specific columns contains a specific value
I have the following df: Data Frame I have not been able to figure out how to delete a row if any of the columns containing the word “test” is less than 95. For example, I would have to delete the entire index row 1 because the column “heat.test” is 80 (the same for rows 0 and 3). In other
I want to select data from different df, how can I speed it up?
I want to take the last data before the specified time from different time intervals df, my code is as follows: On my computer, the running time of get_result_df() is 204ms, how can I speed up the running speed of get_result_df()? I optimized it, and the running time was reduced to 53ms. Is there any room for improvement? Answers to
How to add randomly elements to a column of dataframe (Equally distributed to groups)
Suppose I have the following dataframe: I want to groupby the dataset based on “Type” and then add a new column named as “Sampled” and randomly add yes/no to each row, the yes/no should be distributed equally. The expected dataframe can be: Answer You can use numpy.random.choice: output: equal probability per group: For each group, get an arbitrary column (here
SHAP Linear model waterfall with KernelExplainer and LinearExplainer
I am working on binary classification and trying to explain my model using SHAP framework. I am using logistic regression algorithm. I would like to explain this model using both KernelExplainer and LinearExplainer. So, I tried the below code from SO here This threw an error as shown below AssertionError: Unknown type passed as data object: <class ‘shap.maskers._tabular.Independent’> How can