In this article, we will explore how to create Pandas Data Frames using data from CSV files. We will use the read_csv function, which is a popular API for creating Data Frames by reading data from CSV files.
Key Concepts Explanation
Pandas read_csv Function
The read_csv function is used to create a Pandas Data Frame from a CSV file. Here are some important options that can be used with the read_csv function:
sepordelimiter: Specifies the delimiter used in the CSV file.headerornames: Specifies whether the file has a header or provides a list of column names.index_col: Specifies the column to be used as the index.dtype: Specifies the data types of the columns.
Other APIs for Creating Data Frames
Apart from read_csv, Pandas provides other APIs for creating Data Frames from different types of data sources. Some of these APIs include:
read_fwf: For reading fixed-width formatted lines.read_table: For reading general delimited text files.pandas.io.json: For reading JSON files.
Hands-On Tasks
- Create a Pandas Data Frame for the ‘orders’ dataset from the given CSV file.
- Create a Pandas Data Frame for the ‘order_items’ dataset from the provided CSV file.
Conclusion
In this article, we have learned how to create Pandas Data Frames using the read_csv function and explored other APIs for creating Data Frames from different data sources. It is essential to understand these concepts as they form the foundation of working with data in Pandas.
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