Matplotlib scatter plot with different text at each point

Question

Let's Say I have 3 series

>>> df[df['Type']=="Machine Learning"]['Cost']
0     2300.00
1     3200.00
4     1350.00
7     1352.00
8     4056.00
9       79.00
10    1595.00
Name: Cost, dtype: float64
>>>df[df['Type']=="Machine Learning"]['Rank']
0      1
1      1
4      1
7      2
8      2
9      2
10     2
Name: Rank, dtype: int64
>>>df[df['Type']=="Machine Learning"]['Univ/Org']
0     Massachusetts Institute of Technology 
1     Massachusetts Institute of Technology 
4                                    EDX/MIT
7                        Stanford University
8                        Stanford University
9               Coursera/Stanford University
10                       Stanford University
Name: Univ/Org, dtype: object

Now I want to draw a scatter plot with Cost at the y-axis, Rank at the X-axis, and Name of Univ/Org at each data point.

Now What I am able to do yet after referring to this question is

plt.scatter(df[df['Type']=="Machine Learning"]['Rank'], df[df['Type']=="Machine Learning"]['Cost'],marker='2', edgecolors='black')
for i, txt in enumerate(df[df['Type']=="Machine Learning"]['Univ/Org']):
    plt.annotate(txt, (df[df['Type']=="Machine Learning"]['Rank'][i], df[df['Type']=="Machine Learning"]['Cost'][i]))

It is naming 2 data points and then giving an error.

Plot is :

And Error is:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-111-0d31107a166a> in <module>
      1 plt.scatter(df[df['Type']=="Machine Learning"]['Rank'], df[df['Type']=="Machine Learning"]['Cost'],marker='2', edgecolors='black')
      2 for i, txt in enumerate(df[df['Type']=="Machine Learning"]['Univ/Org']):
----> 3     plt.annotate(txt, (df[df['Type']=="Machine Learning"]['Rank'][i], df[df['Type']=="Machine Learning"]['Cost'][i]))

~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py in __getitem__(self, key)
    869         key = com.apply_if_callable(key, self)
    870         try:
--> 871             result = self.index.get_value(self, key)
    872 
    873             if not is_scalar(result):

~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_value(self, series, key)
   4403         k = self._convert_scalar_indexer(k, kind="getitem")
   4404         try:
-> 4405             return self._engine.get_value(s, k, tz=getattr(series.dtype, "tz", None))
   4406         except KeyError as e1:
   4407             if len(self) > 0 and (self.holds_integer() or self.is_boolean()):

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item()

KeyError: 2

you're not accessing the elements of your dataframe properly. — Paul H
– Paul H, Commented Aug 31, 2020 at 16:40
In other words, 2 isn't in your index. use iloc for positional based indexing — Paul H
– Paul H, Commented Aug 31, 2020 at 16:42

Paul H · Accepted Answer · 2020-08-31 16:47:45Z

1

Couple of things.

First I recommend you select you ML data in to a new dataframe. You should also use the .loc and .at accessors to be a little more precise. So like this:

mldf = df.loc[df['Type'] == "Machine Learning", :]

fig, ax = plt.sunplots()
ax.scatter('Rank', 'Cost', data=mldf, marker='2', edgecolors='black')
for i in mldf.index:
    ax.annotate(mldf.at[i, 'Univ/Org'], (mldf.at[i, 'Rank'], mldf.at[i, 'Cost'])

answered Aug 31, 2020 at 16:47

Paul H

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4 Comments

Ahmad Anis Over a year ago

Thanks, I am using a bit big dataset, so Text is overlapping to each other, Can you suggest how to overcome that.

Paul H Over a year ago

@AhmadAnis I would change the text

Ahmad Anis Over a year ago

You mean the first parameter in ax.annotate?

Paul H Over a year ago

@AhmadAnis yeah

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