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You should be able to use the column name like: df_1 = df_1.drop('furniture')


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In model building there is a sort of iterative workflow that you can use: Select an appropriate model you want to build e.g. for classification maybe a XGB classifier or a logistic regression, etc. This is important because the model by itself will determine a lot about how to wrangle your data. XGB only works with numerical features so you will have to ...


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In general tidy data is great... but it can quickly become unreasonably large. This is the main reason why I usually try to refactor my data in a tidy format as late as possible in the process. Example: imagine a dataset containing $N$ instances, with columns feature1 ... featureX and result1... resultY, where the result? columns represent some value based ...


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Something like this for instance: library(plyr) ddply(data,'region',function(x) {mean(x$age)})


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TO BE EDITTED There would definitely be more optimal ways for addressing this problem. But this is a simple approach. import pandas as pd df = pd.DataFrame({'first': ['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'], 'second': ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two'], 'value': [0.361041, 0....


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I was looking for some sentiment datasets when I encountered a so-called subjectivity analysis on this page: NLP progress sentiment analysis. I thought what I meant can be found in this paper: Distinguishing between facts and opinions for sentiment analysis: Survey and challenges.


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Here, first I created the column values as a list and then assigned them to be the new dataframe's columns. However, we could do it using the columns and without creating lists. It just seems easier this way. #Initializing the new_df (df2) dataframe import pandas as pd new_columns = ['GCF', 'Genome'] new_df = pd.DataFrame(columns = new_columns) #new_df #...


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Initially I did using silence detection but later moved to pyAudioAnalsis which is better. Check "Speaker Diarization" section in Segmentation in pyAudioAnalysis


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This commonly called a "word break" problem. There are a variety of approaches, the most common use dynamic programming or tries. You can recursively try candidates and keep the candidates if they can split the entire string. Here is a version (inspired by this answer): from functools import lru_cache @lru_cache(None) def wordbreak(string): if len(...


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