Exploratory Data Analysis is basically the first look at your data. It mainly focus on understanding the data and its patterns, understanding the relationships between the data and looking at the outliers in your dataset. Exploratory Analysis also helps to find out the missing values that need to be cleaned during cleaning process in the future. EDA helps in gaining insights and formulating hypotheses for further analysis. This article will guide you through the process of EDA using Python's Pandas library, a powerful tool for data manipulation and analysis. We'll cover everything from handling missing values to creating insightful visualizations.

**Data Preprocessing**

Data preprocessing is an important step in the data mining process. It refers to the cleaning, transforming, and integrating of data in order to make it ready for analysis. The goal of data preprocessing is to improve the quality of the data and to make it more suitable for the specific data mining task.

**Getting started with pandas**

Pandas is a powerful and popular data manipulation Library. It is an open source ,easy, fast and intuitive.

Pandas provides powerful tools for cleaning and pre-processing datasets. It also handles missing data, duplicate data and transforms into usable format. Pandas enables data exploration and analysis using correlations, distributions and statistics. It helps in filter, sort, group, aggregate and summarize data. It is also popular for time series analysis and categorical analysis.

The pandas library offers numerous intuitive methods that can be successfully applied for exploratory data analysis. EDA is an important step in any data analytics project. It helps grasp the size and structure of the data, identify the critical variables and potential issues with them, explore the statistics of the data, find the correlation between different columns, and discover hidden patterns in the data for further investigation. EDA in pandas allows running a few lines of simple code to efficiently solve all these tasks and more.

**Lets Explore our Dataset**

First we need to import all libraries and dataset which are required for our analysis, Pandas and NumPy have been used for Data Manipulation and numerical Calculations. Matplotlib and Seaborn have been used for Data visualizations. we have used world population dataset for analysis. Using the** read_ csv()** function, data can be converted to a pandas Data Frame. We have stored the data in the Data Frame df. we have 234 rows and 17 columns in our dataset.

The main goal of data understanding is to gain general insights about the data, which covers the number of rows and columns, values in the data, datatypes, and Missing values in the dataset.

**UNDERSTANDING THE DATA**

**info() **helps to understand the data type and information about data, including the number of records in each column, data having null or not null, Data type, the memory usage of the dataset.

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**DESCRIBING THE DATA**

The** ****describe()** method returns description of the data in the Data Frame.

If the Data Frame contains numerical data, the description contains these information for each column:

count - The number of not-empty values

mean - The average (mean) value

std - The standard deviation

min - the minimum value

25% - The 25% percentile

50% - The 50% percentile

75% - The 75% percentile

max - the maximum value

**FINDING DUPLICATES**

The** nunique ()** method returns the number of unique values for each column. This function counts the number of unique entries in a column of a data frame. It is useful in situations where the number of categories is unknown beforehand.

**MISSING VALUES CALCULATION**

**is null() **is widely been in all pre-processing steps to identify null values in the data. In our example,** data.isnull().sum()** is used to get the number of missing records in each column.

**SORTING ON VALUES**

Pandas** sort _values() **function sorts a data frame in Ascending or Descending order of passed Column.

This will return a new Data Frame sorted by the 'population' column in descending order. The ascending=False argument sorts the column in descending order. If you want to sort in ascending order, you can omit this argument as True is the default value.

**CORRELEATION**

Pandas** df.corr()** is used to find the pairwise correlation of all columns in the Pandas Data frame in Python.

The corr() method calculates the relationship between each column in your data set.

The Result of the corr() method is a table with a lot of numbers that represents how well the relationship is between two columns as below.

The number varies from -1 to 1.1 means that there is a 1 to 1 relationship (a perfect correlation), and for this data set, each time a value went up in the first column, the other one went up as well.0.9 is also a good relationship, and if you increase one value, the other will probably increase as well.-0.9 would be just as good relationship as 0.9, but if you increase one value, the other will probably go down.0.2 means NOT a good relationship, meaning that if one value goes up does not mean that the other__ will.__

**Let's illustrate this with the help of a heat map**

**GROUPING DATA**

This will create a heatmap where the color of each cell represents the correlation coefficient between the corresponding features. The annot=True argument adds the correlation coefficients to the cells in the heatmap.

Grouping your data based on certain criteria can provide valuable insights. For example, you might want to group your data by 'continent' to analyze the data at the continent level. In Pandas, you can use the** ****group by()**** **function to group your data

`grouped_df=df.groupby('continent').mean()`

This will return a new Data Frame where the data is grouped by the 'continent' column, and the values in each group are the mean values of the original data in that group.

**VISUALIZING DATA OVER TIME**

Visualizing your data over time can help you identify trends and patterns. For example, you might want to visualize the population of each continent over time. In Pandas, you can create a line plot for this purpose:

`df.groupby('continent').mean().transpose().plot(figsize=(20, 10))plt.show()`

This will create a line plot where the x-axis represents time and the y-axis represents the average population. Each line in the plot represents a different continent.

**IDENTIFYING OUTLIERS WITH BOXPLOTS**

Box plots are a great way to identify outliers in your data. An outlier is a value that is significantly different from the other values. Outliers can be caused by various factors, such as measurement errors or genuine variability in your data.

In Pandas, you can create a box plot using the** ****boxplot()** function:

**UNDERSTANDING DATA TYPES**

Understanding the data types in your DataFrame is another crucial aspect of EDA. Different data types require different handling techniques and can support different types of operations. For instance, numerical operations can't be performed on string data, and vice versa.

In Pandas, you can check the data types of all columns in your DataFrame using the dtypes attribute:

`df.dtypes`

This will return a Series with the column names as the index and the data types of the columns as the values.

**Why is exploratory data analysis important?**

Exploratory data analysis is important because it helps in understanding the data, identifying trends and patterns, and making informed decisions about further analysis. It allows data analysts to gain insights and discover hidden patterns that can drive business decisions.

## Conclusion

Exploratory Data Analysis (EDA) is a fundamental step in data analytics. It allows you to understand your data, uncover patterns, and make informed decisions about the modeling process. Python's Pandas library offers a wide array of functions for conducting EDA efficiently and effectively.

In this article, we've covered how to handle missing values, explore unique values, sort values, visualize correlations, group data, visualize data over time, identify outliers, understand data types. With these techniques in your data , you're well-equipped to dive into your own data and extract valuable insights.

Remember, EDA is more of an art than a science. It requires curiosity, intuition, and a keen eye for detail. So, don't be afraid to dive deep into your data and explore it from different angles. Hope you all enjoyed this journey!

**Thanks for Reading!**

Looking forward for more topics like this .. thanks for the info