Exploratory Data Analysis in Python: What You Need to Know?

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Exploratory Data Analysis or (EDA) is a technique of numerous discerning aspects of data by outlining their main characteristics and organizing them visually. It involves understanding the data, cleaning it, and analyzing the relationships between variables.

EDA is crucial in every project eminently when we are modeling the data to apply machine learning. Here, data about data,i.e. Metadata, is to take care of. Representations in EDA consist of Histograms, Box plots, Scatter plots, and many more. Data exploration is time-consuming in general. Through EDA, we can filter out our data from redundancies which makes managing and working with data sets a lot easier. There are some great beginner-friendly data analytics courses in Bangalore which you can take up for understanding this concept better.

What is the need for Exploratory Data Analysis?

We perform EDA before we dive into Machine Learning or model our data. This helps us figure out whether the elected features are sufficient to model; all the selected features are required. It is also used to check if there exist any common grounds based on which we have to repeat the Data Pre-processing step or move on to modeling.

Once EDAis finished, and valuable information is received, it is utilized for data modeling. Data is then taken for supervised and unsupervised machine learning.

After finishing every workflow in machine learning, after successfully running the algorithm to completion, the data scientist submits the final report. By the end of EDAyou will have numerous plots, heat-maps, frequency distribution, graphs, correlation matrix, along with the information you obtained from examining your data set. There are some great beginner-friendly data analytics courses in Bangalore that can explain the need for EDA.

What are the steps involved in Exploratory Data Analysis in Python?

There are numerous steps for conducting exploratory data analysis. Following is an overview of those steps:

  • Description of data
  • Handling missing data
  • Handling outliers
  • Recognizing relationships and new insights through plots

Description of data: It is important to know what kinds of data we have before we perform other steps on it. describe() in Python helps with it. In Pandas, we can use describe() on a Data Frame which assists in producing detailed statistics that sum up the central tendency, dispersion, and shape of a dataset’s distribution, besides NaN values. The result will generate an index. The index will introduce count, mean, std, min, max, as well as lower, 50, and upper percentiles. By default, the lower bound of the percentile is 25, and the upper bound is 75. 50 percentile is the median. This is taken into consideration while applying any algorithm.

Handling missing data: Data that we find in the real world is not ideal. It has noise, errors, and gaps. It is generally incomplete. Problems related to missing data must be handled with utmost care because it affects the performance matrix of any machine learning model. It can also point towards wrong prediction or organization and can also affect the performance of a model in numerous ways. There are various options for handling missing values. Some of them as follows:

  • Leave missing values
  • Fill Missing Values
  • Predict Missing values with an ML Algorithm

Handling outliers:As the name signifies, an outlier is something odd or different from the set, something that does not fit the pattern, or that does not follow the general characteristics of a given data set. Outliers can be an outcome of a blunder made during data collection, or it can be an unusual variation in your data. Some of the techniques for identifying and managing outliers are as follows:

  • BoxPlot
  • Scatterplot
  • Z-score
  • IQR(Inter-Quartile Range)

What are the tools required for Exploratory Data Analysis?

There are a lot of open-source tools which perform the process of data analysis through data mining. Some of the most-used ones include MS Advanced Excel, Tableau, Looker, and various others apart from the programming tools.

In programming, we can perform EDA using Python, R, SAS.

Some of the important packages in Python for performing EDA are as follows:

  • Pandas
  • Numpy
  • Matplotlib
  • Seaborn
  • Bokeh

Conclusion

Exploratory Data Analysis is a crucial process. It is a technique of penetrating numerous aspects of data by summarizing their main characteristics and organizing them visually. It involves understanding the data, cleaning the data, and analyzing the relationships between variables. It helps in making data more usable and prepares it for further modeling. Some novice programmers tend to ignore this step which is a big mistake since it affects the performance of any algorithm applied on data sets. There are some beginner-friendly data analytics courses in Bangalore which can help you understand this concept better.

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