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QUESTION
I have a dataset with the name of Danish ministers and their position from 1990 to 2020 (data comes from dataset called WhoGovern; https://politicscentre.nuffield.ox.ac.uk/whogov-dataset/). The dataset consists of the ministers name
, the ministers position
, the prestige
of that position, and the year
in which the minister had that given position.
My problem is that some ministers are counted twice in the same year (i.e., the rows aren't unique in terms of name
and year
). See the example in the picture below, where "Bertel Haarder" was both Minister of Health and Minister of Interior Affairs in 2010 and 2021.
I want to create a dataset, where all the rows are unique combinations of name
and year
. However, I do not want to remove any information from the dataset. Instead, I want to use the information in the prestige
column to combine the duplicated rows into one. The observations with the highest prestige should be the main observations, where the other information should be added in a new column, e.g., position2
and prestige2
. In the example with Bertel Haarder the data should look like this:
(PS: Sorry for bad presenting of the tables, but didn't know how to create a nice looking table...)
Here's the dataset for creating a reproducible example with observations from 2010-2020:
...ANSWER
Answered 2021-Jun-08 at 14:04Reshape the data to wide format twice, once for position
and the other for prestige_1
, and join the two results.
QUESTION
I have two dataframes I need to merge together. The first one is called "rain_year" and the second is called "refugee." rain_year (df1) has a column called "sum_precip". Refugee (df2) has a column called "refugees".
Both dataframes have columns "Year" and "origin" in common and this is what I need to match on. df1 is more than twice the size of df2.
Here is an example of df1:
Year origin sum_precip
1951 Afghanistan 315
1952 Afghanistan 300
1953 Afghanistan 280
and so on and so forth for all countries going back to 1951.
Here is an example of df2:
Year origin refugees
1951 Afghanistan 500
1952 Afghanistan 400
1953 Afghanistan 350
and so on for all countries going back to 1951.
The two dataframes differ in size because not all countries in df2 produced refugees in all years from 1951-present. As such, I expect there to be a considerable amount of 'NA' when merging either df1 to df2 or vice versa.
When I run this code:
...ANSWER
Answered 2021-May-01 at 22:25This one is difficult to spot:
See that in refugee
the name of the Country is "Afghanistan", while in rain_year
it is " Afghanistan": There's a space before the A.
You can solve that like this:
QUESTION
I am trying to write a function to calculate R1 lexical richness measure. The formula is as follows:
R1 = 1 - ( F(h) - h*h/2N) )
where N is the number of tokens, h is the Hirsch point, and F(h) is the cumulative relative frequencies up to that point. my actual data is in the same format as the data below:
...ANSWER
Answered 2021-Apr-21 at 08:26Order them by the integer values in the element names, after stripping the "text" part.
QUESTION
I'm using android studio to create an app, and so I implemented the google maps into my application and everything is working fine, all the implementation are there I triple checked, everything needed for it to operate is there, but my problem is that when I close my application for a long time and reopen the maps activity the fragment showing to me is a random one, no markers no locate button no objects just an empty map with the name of the countries. So I open the google maps application(official application) and close it, then try to reopen my google maps activity and everything reverts to normal, markers showing images are displayed, all the functions operate normally can someone tell me what may be the problem keep in mind everything is added, from an API key to the implementations in the graddle.app and the graddle.app.appname , and in the manifest the metadata is added and so on...thank you !
this is my map activity code where I added some NGOs locations and their images on the map:
...ANSWER
Answered 2021-Mar-23 at 11:50So it turns out everything's coded right but there's one step that I didn't know of, I should've went into the google cloud platform and enabled the google maps SDK for my API key, so I went and did that and then redownloaded the google-services.json
from the firebase (back-end I'm using) and replaced the old one in my app's folder with the new .json file. What was happening is that my API key added was not enabled for external use so when I was opening my google maps application(the official app) my App's API key was substituted by the google maps official one, and that's why my application's app wasn't functioning unless I open the google maps official application and then close it. So I did that and everything's working fine.
QUESTION
I want to retrieve the tables on the following website and store them in a pandas dataframe: https://www.acf.hhs.gov/orr/resource/ffy-2012-13-state-of-colorado-orr-funded-programs
However, the third table on the page returns an empty dataframe with all the table's data stored in tuples as the column headers:
...ANSWER
Answered 2020-Sep-10 at 18:51- For this site, there's no need for
beautifulsoup
orrequests
pandas.read_html
creates a list ofDataFrames
for each
QUESTION
I'm not sure if this is the right place but my test accuracy is always at about .40 while I can get my training set accuracy to 1.0. I'm trying to do a sentiment analysis of tweets on trump, I have annotated each tweet with a positive,negative or neutral polarity. I want to be able to predict the polarity of new data based on my model. I've tried different models but the SVM seems to give me the highest test accuracy. I'm unsure as to why my data model accuracy is so low but would appreciate any help or direction.
...ANSWER
Answered 2020-Apr-12 at 16:03Why are you using naive.score
? I assume it's a copy-paste
mistake. Here are a few steps you can follow.
- Make sure you enough data points and clean it.
Cleaning
the dataset is the inevitable process in data science. - Make use of the parameters like
ngram_range, max_df, min_df, max_features
while featurizing the text with either TfidfVectorizer or CountVectorizer. You may also try embeddings using Word2Vec. - Do a hyperparameter tuning on
alpha, penalty
& other variables using GridSearch or RandomizedSearchCV. Make sure you areCV
currently. Refer the documentation for more info - If the dataset is
imbalanced
, then try using other matrices likelog-loss, precision, recall, f1-score, etc
. Refer this for more info. - Make sure your model is neither
overfitted
notunderfitted
by checking train-error & test error.
Other than SVM
, also try the traditional models like Logistic Regression
, NV
, RF
etc. If you have a large number of data points, then you may try Deep Learning models.
QUESTION
I'm unable to do it by myself, trying it for at least 2 hours...
I have the following data.frame imported from a CSV-file:
...ANSWER
Answered 2020-Apr-24 at 18:08This should set you off in the tidyverse direction to achieve the line graph you aspire to with your data.
QUESTION
I am getting a weird situation in Python 3. Attempting to find some characters (a-z) that have one period and space(s) in between followed by a section sign (0-2 times) followed by a space, followed by digits (1 or more times).
When running this in Python, I don't get a result. It just get hung up.
...ANSWER
Answered 2020-Apr-22 at 17:43If you test your regex in a Python-compatible regex tester, you will see it takes a huge amount of steps to complete matching and ends in catastrophic backtracking. This is caused by the (?:[a-z]+\.{0,1}\s*)+
pattern that is not at the pattern end. It forces the regex engine to backtrack heavily when the consequent (§{0,2})\s+(\d+)
is not found.
The fix is to make the pattern match so that each subsequent pattern could not match the same text at the same location:
QUESTION
The CSS is in a .scss file and being minified with Gulp which works without any issues. I'm testing the responsiveness is Chrome(resizing the webpage). I have added
...ANSWER
Answered 2017-Feb-01 at 20:33QUESTION
I've copied over an XML document below, which is titled 'dictionary.xml' For some reason, the document cannot be parsed by XmlToDict in python.
...ANSWER
Answered 2019-Nov-25 at 18:26Change the quotes from
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