How to create DataFrame from dictionary in Python ?
In this article we will discuss about various ways of creating DataFrame object from dictionaries.
So, let's start exploring different approaches to achieve this result.
- Create DataFrame from Dictionary using default Constructor
- Create DataFrame from Dictionary with custom indexes
- Create DataFrame from Dictionary and skip data
- Create DataFrame from Dictionary with different Orientation
- Create DataFrame from nested Dictionary
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Method-1 : Create DataFrame from Dictionary using default Constructor :
In python DataFrame constructor accepts n-D array, dictionaries etc.
Syntax : pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)
#program :
# import pandas library
import pandas as pd
# dictionary with list object in values
data = {
'Name' : ['Satya', 'Omm', 'Rakesh'],
'Age' : [21, 21, 23],
'From' : ['BBSR', 'RKL', 'KDP']
}
# creating a Dataframe object
df_obj = pd.DataFrame(data)
df_obj
Output : Name Age From 0 Satya 21 BBSR 1 Omm 21 RKL 2 Rakesh 23 KDPAll keys in dictionary are converted to column name and values in dictionaries converted column data.
Method-2 : Create DataFrame from Dictionary with custom indexes :
#program :
# import pandas library
import pandas as pd
# dictionary with list object in values
data = {
'Name' : ['Satya', 'Omm', 'Rakesh'],
'Age' : [21, 21, 23],
'From' : ['BBSR', 'RKL', 'KDP']
}
# creating a Dataframe object
df_obj = pd.DataFrame(data, index = ['a','b','c'])
df_obj
Output : Name Age From a Satya 21 BBSR b Omm 21 RKL c Rakesh 23 KDPBy passing index list, we can avoid the default index.
Method-3 : Create DataFrame from Dictionary and skip data
By skipping some of the items of dictionary, we can also create a DataFrame object from dictionary Let's see the implementation of that.#program :
# import pandas library
import pandas as pd
# dictionary with list object in values
data = {
'Name' : ['Satya', 'Omm', 'Rakesh'],
'Age' : [21, 21, 23],
'From' : ['BBSR', 'RKL', 'KDP']
}
# creating a Dataframe object
#items skipped with key 'Age'
df_obj = pd.DataFrame(data, columns=['name', 'From'])
df_obj
Output : Name From 0 Satya BBSR 1 Omm RKL 2 Rakesh KDP
Method-4 : Create DataFrame from Dictionary with different Orientation
DataFrame can also be created from dictionary usingDataFrame.from_dict() function.
DataFrame.from_dict(data, orient='columns', dtype=None)It accepts orientation too, where we can pass the orientation as index then the keys which were used as columns during creating DataFrame now they will be used as index. Let's see the implementation of that.
#program :
# import pandas library
import pandas as pd
# dictionary with list object in values
data = {
'Name' : ['Satya', 'Omm', 'Rakesh'],
'Age' : [21, 21, 23],
'From' : ['BBSR', 'RKL', 'KDP']
}
# creating a Dataframe object
#items skipped with key 'Age'
df_obj = pd.DataFrame(data,orient='index')
df_obj
Output : 0 1 2 Aame Satya Omm Rakesh From BBSR RKL KDP Age 21 21 23
Method-5 : Create DataFrame from nested Dictionary :
Suppose we have a nested dictionary, then we ill directly pass it in DataFrame constructor where the keys of dictionary will be used as column. Let's see the implementation of that.#program :
# import pandas library
import pandas as pd
# dictionary with list object in values
# Nested Dictionary
data = {
0 : {
'Name' : 'Satya',
'Age' : 21,
'From' : 'BBSR'
},
1 : {
'Name' : 'Omm',
'Age' : 21,
'From' : 'RKL'
},
2 : {
'Name' : 'Rakesh',
'Age' : 23,
'From' : 'KDP'
}
}
# creating a Dataframe object
#items skipped with key 'Age'
df_obj = pd.DataFrame(data)
df_obj
Output : 0 1 2 Aame Satya Omm Rakesh From BBSR RKL KDP Age 21 21 23
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