Python: Float() Argument Must Be A String Or A Number,not 'pandas

Have the following piece of code through which I am trying to plot a graph: import pandas as pd import numpy as np import matplotlib.pyplot as plt import mpld3 my_list = [1,2,3,

Solution 1:

Matplotlib cannot plot category datatypes. You would need to convert to a string.

plt.bar(df2['Range2'].index.astype(str), df2['Range2'].values)

Solution 2:

The pd.cut operation yields intervals:

In[11]: pd.cut(df1["Range1"], [0,1,2,3,4,5,6,7,8,9,10,11,df1['Range1'].max()])
Out[11]:
12      (0, 1]
11      (1, 2]
0       (2, 3]
10      (3, 4]
3       (4, 5]
2       (6, 7]
9       (7, 8]
1       (8, 9]
8     (10, 11]
7     (11, 78]
5     (11, 78]
4     (11, 78]
6     (11, 78]
Name: Range1, dtype: category
Categories (12, interval[int64]): [(0, 1] < (1, 2] < (2, 3] < (3, 4] ... (8, 9] < (9, 10] < (10, 11] <
                                   (11, 78]]

When used in the groupby operation, they are matched based on the index of the cut operation above, and then grouped and summed according to the operation you specified.

As a result, the intervals end up as the index in df2:

In[14]: df2Out[14]:
          Range1Range2Range1
(0, 1]         11
(1, 2]         21
(2, 3]         33
(3, 4]         41
(4, 5]         52
(5, 6]         00
(6, 7]         72
(7, 8]         81
(8, 9]         92
(9, 10]        00
(10, 11]      111
(11, 78]     1694

When you use df2['Range2'].index.values it will be an array of these intervals passed as the first argument to bar, which is not convertible to a float in the way matplotlib expects.

If you are looking to just plot a bar chart of df2.Range2 and you are happy to have the intervals as the axis labels, this will work:

plt.bar(range(len(df2)), df2.Range2.values, tick_label=df2.Range2.index.values)

and produces this image for me: