[Solved] Seaborn load_dataset

I am trying to get a grouped boxplot working using Seaborn as per the example

I can get the above example working, however the line:

tips = sns.load_dataset("tips")

is not explained at all. I have located the tips.csv file, but I can’t seem to find adequate documentation on what load_dataset specifically does. I tried to create my own csv and load this, but to no avail. I also renamed the tips file and it still worked…

My question is thus:

Where is load_dataset actually looking for files? Can I actually use this for my own boxplots?

EDIT: I managed to get my own boxplots working using my own DataFrame, but I am still wondering whether load_dataset is used for anything more than mysterious tutorial examples.

Solution #1:

load_dataset looks for online csv files on https://github.com/mwaskom/seaborn-data. Here’s the docstring:

Load a dataset from the online repository (requires internet).

Parameters


name : str
Name of the dataset (name.csv on
https://github.com/mwaskom/seaborn-data). You can obtain list of
available datasets using :func:get_dataset_names

kws : dict, optional
Passed to pandas.read_csv

If you want to modify that online dataset or bring in your own data, you likely have to use pandas. load_dataset actually returns a pandas DataFrame object, which you can confirm with type(tips).

If you already created your own data in a csv file called, say, tips2.csv, and saved it in the same location as your script, use this (after installing pandas) to load it in:

import pandas as pd

tips2 = pd.read_csv('tips2.csv')
Respondent: Arsibalt
Solution #2:

Just to add to ‘selwyth’s’ answer.

import pandas as pd
Data=pd.read_csv('Pathtocsv')
Data.head(10)

Once you have completed these steps successfully.
Now the plotting actually works like this.

Let’s say you want to plot a bar plot.

sns.barplot(x=Data.Year,y=Data.Salary) //year and salary attributes were present in my dataset.

This actually works with every plotting in seaborn.

Moreover, we will not be eligible to add our own dataset on Seaborn Git.

Respondent: selwyth
Solution #3:

Download all csv files(zipped) to be used for your example from here.

Extract the zip file to a local directory and launch your jupyter notebook from the same directory.
Run the following commands in jupyter notebook:

import pandas as pd
tips = pd.read_csv('seaborn-data-master/tips.csv')

you’re good to work with your example now!

Respondent: Sahil Nagpal
The answers/resolutions are collected from stackoverflow, are licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 .

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