pandas dataframe from dict
data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
pd.DataFrame.from_dict(data)pandas dataframe from dict
data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
pd.DataFrame.from_dict(data)create pandas dataframe from dictionary
In [11]: pd.DataFrame(d.items()) # or list(d.items()) in python 3
Out[11]:
0 1
0 2012-07-02 392
1 2012-07-06 392
2 2012-06-29 391
3 2012-06-28 391
...
In [12]: pd.DataFrame(d.items(), columns=['Date', 'DateValue'])
Out[12]:
Date DateValue
0 2012-07-02 392
1 2012-07-06 392
2 2012-06-29 391python How to convert a dictionary of dictionaries nested dictionary to a Pandas dataframe
# Basic syntax:
dataframe = pd.DataFrame(nested_dictionary)
dataframe = dataframe.transpose()
# Note, this only works if your nested dictionaries are set up in a
# specific way. See below.
# Create nested dictionary:
import pandas as pd
student_data = {
0 : {
'name' : 'Aadi',
'age' : 16,
'city' : 'New york'
},
1 : {
'name' : 'Jack',
'age' : 34,
'city' : 'Sydney'
},
2 : {
'name' : 'Riti',
'age' : 30,
'city' : 'Delhi'
}
}
# Example usage:
pandas_dataframe = pd.DataFrame(student_data)
print(pandas_dataframe)
0 1 2 # Outer keys become column names
age 16 34 30
city New york Sydney Delhi
name Aadi Jack Riti
pandas_dataframe.transpose()
age city name # After transposing, inner keys become column names
0 16 New york Aadi
1 34 Sydney Jack
2 30 Delhi RitiCopyright © 2021 Codeinu
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