What is Pandas: Python For AI Explained

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Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term “panel data”, an econometrics term for data sets that include observations over multiple time periods for the same individuals.

Its key data structure is called the DataFrame, which you can imagine as a relational data table, with rows and columns. The data can be heterogeneously typed. Column names are string and the row index can be anything you want, but most typically it’s an integer or a date/time stamp.

Why Pandas is used in AI

Artificial Intelligence (AI) is a field that has a large impact on both society and the business world. In particular, the area of AI known as machine learning, where computers are taught to learn to do tasks such as decision making and prediction, without being explicitly programmed is a growing field of interest. For AI and machine learning, data is the key ingredient that makes these systems work.

Pandas is a tool that allows us to wrangle and analyze data. It is built on top of two core Python libraries – Matplotlib for data visualization and NumPy for mathematical operations. Pandas takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example). People who are familiar with R would see similarities with R’s data frame syntax.

Handling of data

Pandas is excellent for handling and analyzing data for several reasons. It allows for data cleaning, and provides a host of functions to deal with an array of data manipulations, aggregations, and transformations. It is also great for dealing with missing data, replacing missing or corrupted data, and dropping or filling missing in a dataset.

Moreover, Pandas can be used to read data from a variety of formats such as CSV, TSV, MS Excel, etc. It can also be used to create new derived columns, merge datasets, and perform data summarization using aggregation functions such as group by, rank, maximum, minimum, mean, median, etc.

Data Analysis

Pandas is a popular tool for data analysis in Python for a reason. It provides the necessary data structures for holding data and the data manipulation functions for cleaning and wrangling data. This makes it a convenient tool for data scientists who need to clean, transform, and visualize data. In fact, it is a must-have tool for any data scientist using Python.

From a machine learning perspective, we often split our data into a training set and a test set. The training set is what we feed to the machine learning algorithm to develop a model. The test set is what we use to validate the accuracy of the model. Pandas makes this process simple because it integrates well with packages like Scikit-learn, a machine learning library for Python.

Key Features of Pandas

Pandas provides a host of features that make it a go-to tool for data scientists, statisticians, and data analysts. It provides functionalities to shape, organize, slice, and dice the data. It provides the flexibility to merge, concatenate or reshape the data. It also provides functionality to handle missing data.

One of the most powerful features of Pandas is its DataFrame object for data manipulation with integrated indexing. This object can be used to handle both series and DataFrame. The data manipulation features of Pandas tend to be more sophisticated than in other scientific packages. With Pandas, you can filter out data based on the conditions, slice the data, and modify the data with simplicity.

DataFrame

DataFrame is a two-dimensional labeled data structure with columns of potentially different types. You can think of it like a spreadsheet or SQL table, or a dictionary of Series objects. It is generally the most commonly used pandas object. DataFrame accepts many different kinds of input such as Dict of 1D ndarrays, lists, dicts, or Series; 2-D numpy.ndarray; a Series; or another DataFrame.

Along with the data, you can optionally pass index (row labels) and columns (column labels) arguments. If you pass an index and / or columns, you are guaranteeing the index and / or columns of the resulting DataFrame. Thus, a dict of Series plus a specific index will discard all data not matching up to the passed index. If axis labels are not passed, they will be constructed from the input data based on common sense rules.

Series

A Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The axis labels are collectively referred to as the index. The basic method to create a Series is to call: s = pd.Series(data, index=index)

Here, data can be many different things such as a Python dict, an ndarray, a scalar value (like 5). The passed index is a list of axis labels. Thus, this separates into a few cases depending on what data is:

How to use Pandas in Python

To use Pandas in Python, you first need to install it. If you’re using a Jupyter notebook, you can install it using the following command: !pip install pandas. Once installed, you can import it in your Python script using the following line: import pandas as pd. The “pd” is an alias or shorthand for pandas, which will be used as a prefix before any pandas function.

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Once Pandas is installed and imported in your script, you can use its functions to read, write, analyze, and manipulate data. For example, you can read a CSV file using the pandas read_csv function. If you have a DataFrame df, you can view the first 5 rows using the head function (i.e., df.head()).

Reading Data

To read a CSV file using Pandas, you can use the read_csv function. Here is an example: df = pd.read_csv(‘filename.csv’). This will read a CSV file named filename.csv located in the same directory as your script and store the data in a DataFrame df. If the file is in another directory, you need to specify the full path to the file.

Similarly, you can read an Excel file using the read_excel function, a SQL query using the read_sql_query function, or a SQL table using the read_sql_table function. In each case, the data is stored in a DataFrame that you can then use for data analysis.

Writing Data

Writing data to a file is also straightforward with Pandas. You can write to a CSV file using the to_csv function, to an Excel file using the to_excel function, or to a SQL database using the to_sql function. Here is an example of each:

df.to_csv(‘filename.csv’) # writes to a CSV file
df.to_excel(‘filename.xlsx’) # writes to an Excel file
df.to_sql(‘tablename’, con) # writes to a SQL database

In each case, you need to specify the filename or database table name. For the to_sql function, you also need to specify a connection (con).

Conclusion

Pandas is a powerful Python library for data analysis. It provides the necessary data structures (Series and DataFrame) and data manipulation functions to clean, transform, and visualize data. It is a must-have tool for any data scientist using Python.

Whether you are a seasoned data scientist or a budding one, learning and mastering Pandas will definitely add a valuable tool in your arsenal. Not only it allows you to do data analysis and preprocessing, but it’s also convenient to use for data visualization in conjunction with Matplotlib, another Python library. With its wide range of functionalities, Pandas is a powerful tool for data wrangling and analysis.

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