panda | PANDA open source project | Data Manipulation library
kandi X-RAY | panda Summary
kandi X-RAY | panda Summary
This repository contains the code base for the PANDA project. The PANDA code is composed of a number of C libraries, include files for the API, test code, and sample code.
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QUESTION
I want to add a new column 'BEST' to this dataframe, which contains a list of the names of the columns which meet these criteria:
- Subtract from the current value in each column the value in the row that is 2 rows back
- The column that has the highest result of this subtraction will be listed in 'BEST'
- If more more than one column shares the same highest result, they all get listed
- If all columns have the same result, they all get listed
Input:
...ANSWER
Answered 2021-Jun-16 at 03:33First use shift
and subtract
to get the diff, then replace the maximum values with the column name and drop the others.
QUESTION
I have a data frame including three columns named 'Altitude', 'Distance', 'Slope'. The column of 'Slope' is calculated using the two first columns 'Altitude', 'Distance'. @ the first step the purpose was to calculate 'Slope' using a condition explained below: A condition function was deployed to start from the top column of the "Distance" variable and add up (sum) values until the summation of them is greater or equal to 10 (>=10). If this condition corrects then calculate the "Slope" using the given formula: Slope=Average(Altitude)/(sum(Distance)). The summation of the 'Distance' was counting from the first value of that to the index that the 'Distance' has stopped there). The following code is for the above explanation (By Tim Roberts):
...ANSWER
Answered 2021-May-19 at 13:38Use this code after you calculate s
to get slope column with desired values:
QUESTION
Suppose I have a Pandas dataframe with two identifier columns like this:
...ANSWER
Answered 2021-Jun-15 at 20:56Sounds like a network issue, try with networkx
QUESTION
Consider this dataframe:
...ANSWER
Answered 2021-Jun-15 at 20:30Try:
QUESTION
I have a data frame like this,
...ANSWER
Answered 2021-Jun-11 at 05:56df = df.set_index(["ID", "Rank"])
df = df.unstack("Rank")
df.columns = df.columns.map(lambda col: "_".join(map(str, col)))
QUESTION
I have data which looks like this:
...ANSWER
Answered 2021-Jun-15 at 18:35import numpy as np
features_dict = {
'feat1': np.array([[0,1],[2,3],[4,5]]),
'feat2': np.array([[6,7],[8,9],[10,11]]),
'feat3': np.array([1, 0, 0]),
'feat4': np.array([[1],[2],[1]])
}
ind = features_dict['feat3'] == 0
features_dict = {k: v[ind] for k,v in features_dict.items()}
QUESTION
I have a Graph loaded in pandas and I want to check if my graph has nodes with reciprocity. My dataset looks like this:
id from to 0 s01 s03 1 s02 s01 2 s03 s01The desired output of my code is the reciprocal nodes: (s01, s03)
I found a solution transforming my dataframe into tuples and comparing each combination of my nodes, but I'm sure this solution is far from ideal. Following is my code:
...ANSWER
Answered 2021-Jun-15 at 18:22You can merge the DataFrame with itself after swapping the from and to columns in the right DataFrame. Then sort
the merged result and drop duplicates to get the unique pairs of reciprocal nodes.
QUESTION
I am trying to parse many XML test results files and get the necessary data like testcase name, test result, failure message etc to an excel format. I decided to go with Python.
My XML file is a huge file and the format is as follows. The cases which failed has a message, & and the passed ones only has . My requirement is to create an excel with testcasename, test status(pass/fail), test failure message.
...ANSWER
Answered 2021-Jun-15 at 17:46Since your XML is relatively flat, consider a list/dictionary comprehension to retrieve all child elements and attrib
dictionary. From there, call pd.concat
once outside the loop. Below runs a dictionary merge (Python 3.5+).
QUESTION
I am trying to compute the RMSE of a panda dataframe based on multiple conditions: (plant_name, year, month). My datafram (df3m) looks like this:
...ANSWER
Answered 2021-Jun-15 at 17:13You can use .GroupBy.apply()
and put the call to mean_squared_error
inside it, as follows:
QUESTION
I have this panda series
...ANSWER
Answered 2021-Jun-15 at 17:04You should set the mydata
index to True.
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