Pandas is a software library written for the Python programming language for data manipulation and analysis. Wes McKinney first started this library in 2008 out of the demand for a high performance and flexible tools to perform quantitative analysis on financial data.

Pandas is built on top of two core python libraries. We have the Matplotlib, used for data visualization, and Numpy, used for mathematical operations. With Pandas, Developers can easily work with tabular data (like spreadsheets) within a Python script.

Now, let’s discuss why Pandas library is a useful tool for data exploration and manipulation.

• Data cleansing: It isn’t new that Data Scientist/ Analyst spends a huge amount of time on cleaning datasets and preprocessing to the actual form they want. Pandas provides an easy pathway to be able to handle this messy data, deal with missing values and remove irrelevant data. With pandas we can read, process and write to (CSV, TSV, Excel, HDF, JSON, THML , database data files) and any other formats. Pandas gives us two different forms of representing our data. We have the Series, a list-like structure, and DataFrames, which has a tabular structure.
• Data Exploration: Data exploration refers to the initial step in data analysis in which Data Analysts use data visualization and statistical techniques to describe dataset characterizations. In this phase, a data analyst can explore and identify relationships between different data variables, the presence of outliers and what the structure looks like. Data Exploration is usually carried out to get insight and recognize patterns from the data. With that being said, Pandas is an excellent tool when it comes to Data Exploration in a dataset. All Data Scientists/ Analysts should have this in their skill set.
  Here are different functions we can use in pandas for exploring data;
• .info(): The info function is used to return information about our DataFrame. The information contains the number of columns, column labels, column data types, memory usage, range index, and the number of cells in each column.
• .head(): With this function, we can print the first 6 rows of our data and change it to print other values . i.e df.head(10); this will print the first 10 rows of data in our DataFrame.
• .tail(): We can use .tail() in the reverse order of .head(). Here we’ll print the last 6 rows of our data, or the tail end of our data.
• .describe(): We can use the .describe() method to calculate statistical data such as the Percentile, Mean & Standard Deviation of numerical values in our DataFrame or series.
• .sort_values(): When working with our data, a need might arise for us to want to sort our DataFrame in a particular order. We can sort by column, ascending order and descending order.
• .groupby(): the .groupby() function is usually used to split data into separate groups, so as to perform computations such as; .mean(), .sum() e.t.c for better analysis
• Data visualization: Pandas is one of the efficient ways to visualize data. It provides several different functions to use with the help of the .plot() function. With this .plot() function we can plot all kinds of graphs such as Bar graph, Histogram, Box Plot, Area Plot, Scatter Plot e.t.c. when working with series or DataFrame.

Bar graph — df.plot(kind="bar")

Histogram — df.plot.hist()

Box Plot — df.plot.box()

Area Plot — df.plot.area()

Scatter Plot — df.plot.scatter();

• Merges and Joins: Another way Pandas is helpful is the ability to merge and join datasets. Pandas provides us an effective method for easily combining DataFrame or Series based on different forms of logic. With pandas, we can merge, join, and concatenate in our datasets, thereby providing a meaningful structure for our dataset.

.merge() — Pandas use the .merge() method to combine two different datasets. We can also merge with any join types(inner, left, right, and outer).

.join() — Here also, pandas uses .join() method to combine differently-indexed DataFrames into a new DataFrame. We can join using the argument ‘on’ , or join on two differently indexed DataFrames.

.concatenate() — The .concat() method is a very efficient way of handling large datasets. We can concatenate DataFrames both vertically(axis=0) and horizontally(axis=1).

• Data Normalization: Another area where we use Pandas is the aspect of Data Normalization. Data analysts or scientists usually carry out normalization to adjust values existing on different scales to a common scale.
  The Pandas library provides multiple built-in methods through maximum absolute scaling which makes data normalization techniques very easy to implement. Some of these methods includes;

.max() method

.abs() method

• Statistical Analysis: Pandas has also proven many times as the best tool for carrying out statistical analysis of data. In Pandas, different built-in statistics are made available, which we can apply to our numerical data. Some of these methods we can use are listed below;

.value_counts() — This function returns object containing counts of unique values

.count() — The count() method is used for counting the number of not empty values for each row, or column.

.mean() — The mean() method is used to calculate the mean of the values for the requested axis.

.median() — The median() method is used to calculate the median values for the requested axis.

.argmax() — This can be used to calculate the maximum value present in the input Index. If we have more than one maximum value (i.e., the maximum value is present more than once), then it returns the index of the first occurrence of the maximum value.

.max() — This is used to calculate the maximum value of the requested axis.

Hope you enjoyed this article. Thanks for reading.