Plotting a bar graph using matplotlib or seaborn from python nested dictionary of two datasets for comparison

Question

I have below two dictionaries:

data1 = {
    "value-A" : {
        "mean" : 10.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 2.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 6.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 11.4,
        "count" : 200 
    }
}

and

data2 = {
    "value-A" : {
        "mean" : 20.0,
        "count" : 50 
    },
    "value-B" : {
        "mean" : 6.0,
        "count" : 100 
    },
    "value-C" : {
        "mean" : 18.6,
        "count" : 150 
    },
    "value-D" : {
        "mean" : 30.4,
        "count" : 120 
    }
}

I have below questions:

1. How can I plot a bar graph for single dictionary?
2. How can plot a comparison bar graph for mean values from two different dictionary like above? I am looking something like below snapshot

Answer 1

You can use pyplot.bar to make a bar plot. You can check the documentation for more details about it, but you need the location of the bars (in your case, the value_x_mean variables) and the height of the bars (value_x_count). I suggest renaming your parameters inside the dictionary to make iterating through it easier:

data = {
"value-A" : {
    "value-a-mean" : 10.0,
    "value-a-count" : 100 
},
"value-B" : {
    "value-b-mean" : 2.0,
    "value-b-count" : 150 
},
"value-C" : {
    "value-c-mean" : 6.6,
    "value-c-count" : 220 
},
"value-D" : {
    "value-d-mean" : 11.4,
    "value-d-count" : 200 
    }
} 


for key, value in data.items():
    for sub_key, sub_value in value.items():
        # When you do 'value_a_mean'.split('-') you get ['value', 'a', 'mean']
        # We take the last item from this list [-1], so just 'mean' or 'count'
        value[sub_key.split('-')[-1]] = value[sub_key]
        del value[sub_key]

Now data will look like this:

{
    "value-A" : {
        "mean" : 10.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 2.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 6.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 11.4,
        "count" : 200 
    }
} 

Then you can do:

from matplotlib import pyplot

data = {
    "value-A" : {
        "mean" : 10.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 2.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 6.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 11.4,
        "count" : 200 
    }
} 

means, counts, labels = [], [], []

# Read the dictionary and save values
for key, value in data.items():
    labels.append(key)
    means.append(value['mean'])
    counts.append(value['count'])

# Bar plot
pyplot.bar(means, counts, tick_label=labels)
pyplot.show()

Result:

You can do this for as many other dictionaries as you want and the bar plots will show up together. You can use a legend to label them:

# Random 2nd dictionary
data2 = {
    "value-A" : {
        "mean" : 20.0,
        "count" : 100 
    },
    "value-B" : {
        "mean" : 3.0,
        "count" : 150 
    },
    "value-C" : {
        "mean" : 5.6,
        "count" : 220 
    },
    "value-D" : {
        "mean" : 8.4,
        "count" : 200 
    }
} 

means, counts, labels = [], [], []

for key, value in data.items():
    means.append(value['mean'])
    counts.append(value['count'])

pyplot.bar(means, counts, label="Dataset 1")

means, counts, labels = [], [], []

for key, value in data2.items():
    means.append(value['mean'])
    counts.append(value['count'])

pyplot.bar(means, counts, label="Dataset 2")
pyplot.legend()
pyplot.show()

Result: