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Transcription Job: Python Programming Lecture - Pandas DataFrames and read_csv Client: Eagle Web Transcribed by: Luciah Kemunto Date: 12/11/2025 Audio Length: 05:16 minutes Male_1: [00:00:00.000 – – > 00:00:11.000] Data frame. Again, all these broadcasting operations are extremely fast. They are backed by the NumPy array and result in a Series. Male_1: [00:00:11.000 – – > 00:00:26.000] You can get quick statistical information using methods like describe, min, max, mean, and median. All work as expected. Male_1: [00:00:26.000 – – > 00:00:47.000] In pandas, you have the concept of a DataFrame with multiple columns and rows. These operations result in just one Series. Male_1: [00:00:47.000 – – > 00:01:15.000] In pandas, you have your DataFrame, and you have your Series, which contain individual numbers. The DataFrame often resorts to Series; some operations return a Series, which can be used in a DataFrame. Male_1: [00:01:15.000 – – > 00:01:32.000] In this case, these operations resulted in a Series. We then immediately used the Series to set the value of a column. That is why understanding Series is so important. Male_1: [00:01:32.000 – – > 00:01:49.000] There are a few more exercises for you here. Complete them to make more sense of it when you are working with it. Male_1: [00:01:49.000 – – > 00:02:05.000] Finally, a quick introduction to reading external data and plotting. We will use the read_csv function from pandas. Male_1: [00:02:05.000 – – > 00:02:24.000] Pandas has several read functions: read_sql, read_excel, read_xml, read_json, and read_html, which can parse an HTML page.[inaudible] Male_1: [00:02:24.000 – – > 00:02:43.000] These functions import data from an external source into a pandas workflow. We will read the BTC market price data. Male_1: [00:02:43.000 – – > 00:03:03.000] The CSV file contains a timestamp and a value. The price data starts from 2017. Male_1: [00:03:03.000 – – > 00:03:24.000] We use the read_csv method to automatically parse the CSV. We will now tune the process to get it right. Male_1: [00:03:24.000 – – > 00:03:46.000] I will show a few customizations for the read_csv function. There are a ton of attributes; you will not remember them all. Male_1: [00:03:46.000 – – > 00:04:10.000] Do not worry; you can always refer to the documentation. The first row of the CSV was considered the column names. Male_1: [00:04:10.000 – – > 00:04:36.000] This file does not have column names. By default, pandas assumes the first line is the header. We will change that assumption. Male_1: [00:04:36.000 – – > 00:04:49.000] We will use header=None. This is one of the attributes we use from the read_csv function. Male_1: [00:04:49.000 – – > 00:05:07.000] Using header=None means do not infer a header from the CSV file. And the columns are... [inaudible]

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