# Tag Info

10

Don't worry - your hard-earned Python skills are still important ;) Tableau is not a replacement - it is essentially a means of sharing your insights/findings. It is a wrapper around your normal toolkit (Pandas, Scikit-Learn, Keras, etc.). It can do some basic analysis (just using basic models from sklearn), but the powerful thing is it can deploy your ...

6

There is the official answer and the realistic answer (from a business perspective): Official Officially the greatest thing your Python skills will bring you is flexibility. If you are going to run some economical model where you want to show a gradient uncertainty or something else crazy, doing that manually in any Data Visualization/Business Intelligence ...

5

Those symbols you are talking about are hex values. They could actually be useful to keep in the dataset, as the tcpdump tool outputs them if the right options are set. Despite that, you can replace occurrences of them using pandas and a simple regular expression. A regular expression that matches hex values is: 0[xX][0-9a-fA-F]+ I downloaded one of the ...

3

First: I think you want the product functionality, not zip, since you are checking every df with every ref. In zip, you would check df_a with ref_1 and df_b with ref_2 only. Second: Your can look at the equation $(1+2+3+4)−(5+5+5+5)$ as $(1-5) + (2-5) + ...$ which is simply subtracting data frames and sum over columns. With these two consideration, ...

2

Those are hex numbers, aka base 16 digits. Hex numbers in Python are represented as strings that start with "0x". Either keep them as hex or convert them to base 10 integers: int("0x000b", 16) #=> 11 int("0xc0a8", 16) #=> 49320

2

Good morning, There is nothing wrong with your data, your main mistake is that you need to pay attention to the plot function used. This link tells you how to plot your data. https://matplotlib.org/tutorials/introductory/pyplot.html However I would change the last line to something like: import matplotlib.pyplot as plt plt.plot(y_rus) plt.show() or ...

2

I assume you a list of the filenames called movie_images # Could get filenames with: # import os; movie_images = os.listdir("./folder/with/images/") movie_filenames = ["11_lion_king.jpg", "22_avengers.jpg"] First create a mapping between the ID values and the filenames: # Use the "_" to split the filename and take the first items, the ID mapping = {f....

1

LabelEncoder is not made to transform the data but the target (also known as labels) as explained here. If you want to encode the data you should use OrdinalEncoder. If you really need to do it this way : categorical_cols = ['a', 'b', 'c', 'd'] from sklearn.preprocessing import LabelEncoder # instantiate labelencoder object le = LabelEncoder() # apply ...

1

Slight addendum to the above solution: ##First create a mapping between the ID values and the filenames: # Use the "_" to split the filename and take the first items, the ID mapping = {f.split("_")[0]: f for f in movie_filenames} # <-- a dictionary-comprehension ##Now iterate over this mapping, inserting the movie filenames for the correct movie ...

1

As someone who worked on a competitor to Tableau, Data Science skills have largely superseded the need for Bi Software for data munging, complex analysis and ad hoc reports. But BI Software can still be beneficial if you need to deploy your results to lots of people, often with varying rights to view something (e.g. you can only see your performance stats, ...

1

Well, it always depends, for example, on what model you might be training (i.e. some are robust to multicollinearity). I am pretty sure you are aware, but to have it said as a rule of thumb it is always helpful if you know what you are looking for, rather than hoping naively one function or method would give all the answers. Said that, there are good ...

1

What you want to do is exactly the default behavior of the category type. Convert your month value to the type category declaring all months (it has a somewhat weird interface to create a categorical type) df.month= dd.month.astype(pd.api.types.CategoricalDtype(categories=range(12))) df.month.value_counts() will give you: id code month sally s_A ...

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