pythonpandasmatplotlibseaborngrouped-bar-chart

How to combine different DataFrames into one grouped bar plot

I'm trying to copy the answer from this question, I can't see what I'm doing that's different.

My output, similar to that question or here is a grouped bar plot, where this data frame:

    Area  Number   Cat
0  item1       4  Cat1
1  item2       6  Cat1
2  item3       7  Cat1
3  item4       8  Cat1
4  item5      10  Cat1
0  item1       4  Cat2
1  item2       6  Cat2
2  item3       7  Cat2
3  item7       8  Cat2
4  item6      10  Cat2

Is grouped by area, and then each area has two conjoined bars showing the number for Cat1 and Cat2. So the output should have seven groups of bars (i.e. item 1,2,3,4,5,6,7) and each group has two bars in it, one for category 1 and one for category 2.

This code:

import sys
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

test_dict1 = {'item1':4, 'item2':6, 'item3':7,'item4':8,'item5':10}
test_dict2 = {'item1':4, 'item2':6, 'item3':7,'item7':8,'item6':10}

#df = pd.DataFrame({'test_dict1':pd.Series(test_dict1),'test_dict2':pd.Series(test_dict2)}).reset_index()
#df = df.set_axis(['Item', 'DictOne', 'DictTwo'], axis=1)

test1_df = pd.DataFrame(test_dict1.items(), columns=['Area','Number'])
test2_df = pd.DataFrame(test_dict2.items(), columns=['Area','Number'])


test1_df['Cat'] = 'Cat1'
test2_df['Cat'] = 'Cat2'

dfs = [test1_df,test2_df]
result = pd.concat(dfs)

print(result)

from matplotlib import rcParams
sns.set(style="whitegrid")
rcParams.update({'figure.autolayout': True})
fig, ax1 = plt.subplots(figsize=(12,6))

result.plot.bar(x='Cat', ax=ax1)
plt.tight_layout()
plt.xticks(rotation=45, ha="right")
plt.show()

produces this output:

enter image description here

Can someone show me where I'm going wrong?

Solution 
  • >>> import seaborn as sns
>>> sns.barplot(data=result, x='Area', y='Number', hue='Cat')

    >>> result = result.pivot_table(index='Area', columns="Cat", values="Number")

Cat    Cat1  Cat2
Area             
item1   4.0   4.0
item2   6.0   6.0
item3   7.0   7.0
item4   8.0   NaN
item5  10.0   NaN
item6   NaN  10.0
item7   NaN   8.0

>>> ax1 = result.plot(kind='bar', rot=0, figsize=(12,6))

    enter image description here