after predicting the target value for the classification problem trying to get the predicted values in a .csv file along with the id of particular data instance but getting the unnecessary row numbers. x_test.head() image to.csv() image Answer You index=False , doc specifies this argument
Tag: dataframe
regex match not working on simple string with Pyteomics parser
I am performing an in silico digestion of the human proteome, meaning that I am trying to chopped the amino acid sequence of every protein at a certain position. I am using the Pyteomics parser function Pyteomics Parser within a bigger function that I have created. I am getting this error: PyteomicsError: Pyteomics error, message: “Not a valid modX sequence:
how to compare two columns and get the mean value of the the 3rd column for all matching items in the two in python pandas dataframe?
I have the following table named Rides : start_id end_id eta A B 5 B C 4 A C 6 A B 5 B A 3 C A 3 B C 6 C A 5 A B 8 From the Rides Table , I want to Create a new table which should look like something like below : start_id end_id
Are optimizations possible when finding duplicates in pandas dataframe based on array overlap?
I’ve a pandas dataframe that contains information about meetings. I need to find duplicate entries based on certain conditions. Let’s look at a sample dataframe first. The meeting-id is a unique identifier for a meeting. Brokers are the attendees of these meetings. The catch is, brokers are not always honest, and many of them can enter same meeting info multiple
How to slice/chop a string using multiple indexes in a panda DataFrame
I’m in need of some advice on the following issue: I have a DataFrame that looks like this: And what I need to get is the SEQ that’s separated between the different BEG_GAP and END_GAP. I already have worked it out (thanks to a previous question) for sequences that have only one pair of gaps, but here they have multiple.
Select entries in one dataframe based on cross-sectional statistic of another dataframe
I want to select the entries of one dataframe, say df2, based on the cross-sectional statistic of another dataframe, say df1: For instance, if the cross-sectional statistic on df1 is a max operation, then for the 3 rows in df1 the corresponding columns with the max entries are ‘D’, ‘C’, ‘B’ (corresponding to entries 11, 45, 314). Selecting only those
Parsing nested JSON with list comprehension in Python
My data is as following (this just extract but there are much more objects, some don’t have the additionalData) I’m trying to iterate with list comprehension to get dataframe of referenceDataItems and everything within that key, also additionalData if appears. Expected result: Answer I did some research and this almost got my desired data, needs little modification in COLUMNS_TO_DROP
Two-point Euclidean distance from csv file
I want to calculate the distance between two points and label them. The problem is that the code doesn’t work on more than 1 line. When there is 1 row, the program shows me result which I want: This is an error when there is more than 1 line : “cannot convert the series to <class ‘float’>” This is my
Plotting graph from data frame
Plotting the graph for both South Asia and Eastern Asia using the above function is showing the same countries and same graphs .What mistake am I doing while writing the above code, I can’t figure that out? enter image description here Answer The problem is with your function. Remove the for loop and it should work
How to assign value to particular column in pandas dataframe based on different conditions?
I have a dataset with around 40,000 rows each representing a record in dataset. One of the features named ‘region_code’ is categorical in nature but is represented using integer. It is similar to pincode/zipcode. There are around 5316 unique ‘region_code’ values and these Region_Codes start from 1 and go upto 5690. That means, range is [1,5690]. I want to reassign