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A dictionary containing other dictionaries is called a nested dictionary.
Example:
countries = {
1: { Country: "India", Capital: "Dehli", Population: "1,414,681,270" },
2: {
Country: "United State",
Capital: "Washington, D.C.",
Population: "335,962,130",
},
3: { Country: "China", Capital: "Beijing", Population: "1,453,523,041" },
}
we have created 3 dictionaries inside a dictionary “countries”. We then assigned these dictionaries to the values of the respective keys, 1 and 2, and 3.
method1: Using pandas dataframe from a dictionary as given below
pd.DataFrame.from_dict(mydic)
In the pandas’ document, The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default) Otherwise, if the keys should be rows, pass ‘index’. So, We use the keyword argument orient as 'index' because keys 1 and 2, and 3 are associated with three dictionaries inside the dictionary are treated as rows. and key inside each dictionary as columns by default.
countries={"1":{"Country": "India",
"Capital": "Dehli",
"Population": "1,414,681,270"},
"2":{"Country": "United State",
"Capital": "Washington, D.C.",
"Population": "335,962,130"},
"3":{"Country": "China",
"Capital": "Beijing",
"Population": "1,453,523,041"}}
df =pd.DataFrame.from_dict(countries,orient='index')
df
Output:
Country Capital Population
1 India Dehli 1,414,681,270
2 United State Washington, D.C. 335,962,130
3 China Beijing 1,453,523,041
method 2:using pandas.dataframe, it converts each assigned key into columns and keys inside three dictionaries into an index:
countries={"1":{"Country": "India",
"Capital": "Dehli",
"Population": "1,414,681,270"},
"2":{"Country": "United State",
"Capital": "Washington, D.C.",
"Population": "335,962,130"},
"3":{"Country": "China",
"Capital": "Beijing",
"Population": "1,453,523,041"}}
df = pd.DataFrame(countries)
df
Output:
1 2 3
Country India United State China
Capital Dehli Washington, D.C. Beijing
Population 1,414,681,270 335,962,130 1,453,523,041
while using the T property transpose the index and columns of the data frame,
countries={"1":{"Country": "India",
"Capital": "Dehli",
"Population": "1,414,681,270"},
"2":{"Country": "United State",
"Capital": "Washington, D.C.",
"Population": "335,962,130"},
"3":{"Country": "China",
"Capital": "Beijing",
"Population": "1,453,523,041"}}
df = pd.DataFrame(countries).T
df
Output:
Country Capital Population
1 India Dehli 1,414,681,270
2 United State Washington, D.C. 335,962,130
3 China Beijing 1,453,523,041
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