Pandas is a Python library widely used for data manipulation and analysis. It provides efficient data structures for data processing tasks and is highly regarded in the data science community.
In this article, we will explore some of the essential operations of pandas, such as getting values from pandas series and other common operations in pandas like filtering, data aggregation, and data visualization.
Getting Values from Pandas Series
There are various methods to extract values from pandas Series. Below are some of the popular methods:
Method 1: Using Index
You can access a specific value in pandas series using the index of that value.
This method is particularly useful when you know the exact location of the value. It is done by:
For example, if you have a series of temperatures like this:
temperatures = pd.Series([22, 24, 19, 25, 26, 18])
You can extract the value at index 3 by:
Method 2: Using String
If you have a series of strings and want to extract a specific string, you can use the .loc function of pandas. Here’s how:
pandas_series.loc[pandas_series == ‘specific_string’]
For instance, if you have a pandas series of the names of countries, you can extract the name ‘USA’ as follows:
countries = pd.Series([‘Russia’, ‘China’, ‘India’, ‘USA’, ‘Canada’])
countries.loc[countries == ‘USA’]
Method 3: Using Pandas DataFrame
You can extract values from a pandas series using a pandas dataframe. This works best when you want to extract a value that is associated with another value in the dataframe.
You can achieve this using the .loc or .values function. 1.
2. pandas_dataframe.values[row_index, column_index]
Here’s an example.
Suppose you have a dataframe with the name “stocks” that has columns of companies, prices, and volumes. You could extract the price of a company, say ‘Apple’, on a particular day as follows:
stocks.loc[stocks[‘Company’] == ‘Apple’, ‘Price’]
Other Common Operations in Pandas
Apart from value extraction, pandas has many other important operations that data analysts regularly use. Some of these operations include:
Pandas enables analysts to filter data based on certain conditions. You can filter data by rows, columns or both.
This is done using the .loc and .iloc functions. Here’s how:
For filtering rows:
2. For filtering columns:
b. pandas_dataframe.iloc[:, column_index]
Data aggregation aims to bring together different data in a format that is easier to analyze. Pandas provides a groupby() function for performing data aggregation.
You can apply different aggregation functions like mean(), sum(), count(), etc., to the data to obtain meaningful information.
Data visualization is an important aspect of data analysis as it allows for easy interpretation of the data. Pandas provides a plot() function that makes it easy to create a line plot, scatter plot, histogram, and other types of plots.
In conclusion, this article covered some of the essential functions of pandas, including getting values from pandas series and other common operations like filtering, data aggregation, and data visualization. These operations are useful for analyzing data and delivering insights to businesses.
With pandas, analysts can efficiently manipulate data and produce meaningful results. In this article, we explored the essential operations of pandas, including getting values from pandas series and other common operations like filtering, data aggregation, and data visualization.
We saw how these operations are useful for analyzing data and delivering insights to businesses. By utilizing pandas, analysts can efficiently manipulate data and produce meaningful results.
Takeaways include leveraging pandas for easy indexing, filtering, and aggregation of data based on specific conditions. Additionally, data visualization aids in clear interpretation of data, and pandas provides useful tools for creating this.
Overall, pandas remains one of the most popular libraries for data analysis and is critical for any data scientist.