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Initialising a DataFrame using a dictionary in Pandas
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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Using a dictionary of arrays
To create a DataFrame using a dictionary of arrays:
df = pd.DataFrame({"A":[3,4], "B":[5,6]})df
A B0 3 51 4 6
Here, the key-value pair of the dictionary is as follows:
key: column labelvalue: values of that column
Also, since the data does not contain any index (row labels), the default integer indices ([0,1]) are used.
Using a nested dictionary
To create a DataFrame using a nested dictionary:
col_one = {"a":3, "b":4}col_two = {"a":5, "b":6}df = pd.DataFrame({"A":col_one, "B":col_two})df
A Ba 3 5b 4 6
Here, we've specified the index in col_one and col_two.
Using a dictionary whose key-value pair represents a row
Consider the following dictionary that holds some data:
data = { "alex": 20, "bob": 30, "cathy": 40}
We want to create a DataFrame whose values are all the key-value pairs of data. Pandas does not provide a direct solution for this, so we ourselves must extract the keys and values of data as lists:
arr_name = list(data.keys())arr_age = list(data.values())
We can then use the first approach of initialising a DataFrame using a dictionary of arrays:
df = pd.DataFrame({"name":arr_name, "age":arr_age})df
name age0 alex 201 bob 302 cathy 40
Published by Isshin Inada
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