Pandas is a powerful data analysis and manipulation library for Python. One of the core data structures in Pandas is the DataFrame, a two-dimensional table-like structure that contains rows and columns of data. In this article, we will explore how to create data frames in Pandas using dictionaries.
Creating DataFrames with Dictionaries
One of the simplest ways to create a data frame in Pandas is by using a dictionary. In a dictionary, we can specify column names as keys and lists or arrays of data as values. Here's an example:
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import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'Dave'],
'Age': [25, 32, 18, 47],
'Country': ['USA', 'Canada', 'UK', 'Australia']}
df = pd.DataFrame(data)
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In this example, we create a dictionary called data that contains three keys: Name, Age, and Country. The values of each key are lists that contain the data for each column. We then use the pd.DataFrame() function to create a data frame called df from this dictionary.
By default, Pandas will assign an index to the rows of the data frame starting from 0. We can also specify a custom index by passing a list of index values as a parameter:
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import pandas as pd
data = {'Name': ['Alice', 'Bob', 'Charlie', 'Dave'],
'Age': [25, 32, 18, 47],
'Country': ['USA', 'Canada', 'UK', 'Australia']}
index = ['A', 'B', 'C', 'D']
df = pd.DataFrame(data, index=index)
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In this example, we create a custom index by passing a list of index values to the pd.DataFrame() function as a parameter.
We can also create a data frame from a dictionary of dictionaries. In this case, the keys of the outer dictionary correspond to the column names, and the keys of the inner dictionaries correspond to the row labels. Here's an example:
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import pandas as pd
data = {'Name': {'A': 'Alice', 'B': 'Bob', 'C': 'Charlie', 'D': 'Dave'},
'Age': {'A': 25, 'B': 32, 'C': 18, 'D': 47},
'Country': {'A': 'USA', 'B': 'Canada', 'C': 'UK', 'D': 'Australia'}}
df = pd.DataFrame(data)
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In this example, we create a dictionary of dictionaries called data. The keys of the outer dictionary are the column names, and the keys of the inner dictionaries are the row labels. We then use the pd.DataFrame() function to create a data frame called df from this dictionary.
Conclusion
In this article, we've learned how to create data frames in Pandas using dictionaries. By specifying column names as keys and data as values, we can easily create a data frame from a dictionary. We can also specify a custom index by passing a list of index values as a parameter. Additionally, we can create a data frame from a dictionary of dictionaries, where the keys of the outer dictionary correspond to the column names and the keys of the inner dictionaries correspond to the row labels. With these techniques, we can quickly and easily create data frames in Pandas to work with in our data analysis and manipulation tasks.