Hello! In this lesson, we will explore the fundamental techniques for inspecting financial data using the Pandas library in Python. Our goal is to enable you to load financial data, inspect its structure, and perform basic data analysis. Let's get started!

First, let's recap how to import the necessary libraries and load the dataset. In this scenario, we'll use Tesla (TSLA) historical stock prices.

1. Import Libraries: We need to import pandas for data manipulation and the datasets library to load our data.
2. Load the Dataset: We use the load_dataset function from the datasets library to load the Tesla dataset.
3. Convert to DataFrame: We convert the loaded dataset into a Pandas DataFrame.
4. Display Data: Using the head() and tail() methods, we can view the first few and last few rows of the dataset, respectively.

Here's the code to achieve this:

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1import pandas as pd
2import datasets
3
4# Load TSLA dataset
5tesla_data = datasets.load_dataset('codesignal/tsla-historic-prices')
6tesla_df = pd.DataFrame(tesla_data['train'])
7
8# Display first 5 rows of the DataFrame
9print(tesla_df.head())

This code snippet loads the TSLA dataset and displays the first 5 rows to help us get a quick look at the data.

Next, we want to understand the structure of our dataset. This involves examining the columns, data types, and the number of non-null entries. The info() method of a Pandas DataFrame provides a concise summary of these details.

• Data Structure Information: The info() method reveals important aspects such as:
  • Column names and data types
  • Non-null counts for each column

Here's the code to inspect the data structure:

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1# Print basic information about the dataset
2print(tesla_df.info())

The output will be:

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1<class 'pandas.core.frame.DataFrame'>
2RangeIndex: 3347 entries, 0 to 3346
3Data columns (total 7 columns):
4 #   Column     Non-Null Count  Dtype  
5---  ------     --------------  -----  
6 0   Date       3347 non-null   object 
7 1   Open       3347 non-null   float64
8 2   High       3347 non-null   float64
9 3   Low        3347 non-null   float64
10 4   Close      3347 non-null   float64
11 5   Adj Close  3347 non-null   float64
12 6   Volume     3347 non-null   int64  
13dtypes: float64(5), int64(1), object(1)
14memory usage: 183.2+ KB
15None

This output summarizes the dataset structure, showing that it consists of 3347 entries with 7 different columns. It also highlights that there are no missing values in the dataset, and it provides the data type of each column, which is essential to understand before performing any data manipulation or analysis.

To gain preliminary insights into our data, we can use the describe() method, which provides summary statistics such as mean, standard deviation, minimum, and maximum values, and quartiles.

• Descriptive Statistics: The describe() method presents these key statistics for all numerical columns in the DataFrame, helping us understand data distribution and identify any anomalies.

Here's the code to generate summary statistics:

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1# Display summary statistics
2print(tesla_df.describe())

The output will be:

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1              Open         High  ...    Adj Close        Volume
2count  3347.000000  3347.000000  ...  3347.000000  3.347000e+03
3mean     67.901248    69.413435  ...    67.886520  9.643192e+07
4std     100.209872   102.472746  ...   100.136888  8.058132e+07
5min       1.076000     1.108667  ...     1.053333  1.777500e+06
625%      10.152000    10.432000  ...    10.081333  4.540425e+07
750%      16.793333    17.000000  ...    16.771334  8.011650e+07
875%      66.069336    67.129334  ...    65.896000  1.230548e+08
9max     411.470001   414.496674  ...   409.970001  9.140820e+08
10
11[8 rows x 6 columns]

This concise summary details the distribution of Tesla's stock prices, including the mean, standard deviation, minimum, and maximum values across various metrics such as opening price, high, low, close, adjusted close, and volume. It provides a snapshot of the stock's volatility and trading volume, which are critical for financial analysis.

In this lesson, you have learned the basics of data inspection using Pandas. We have covered how to:

• Load a dataset and convert it into a DataFrame.
• Display the data using the head() method.
• Inspect the data structure using the info() method.
• Generate summary statistics using the describe() method.

These fundamental skills are crucial for analyzing financial data and making informed trading decisions. Practice exercises will follow to reinforce your understanding and improve your data handling proficiency. Let's keep up the momentum and continue mastering financial data handling in Pandas!