When a new DataFrame is received for work, the first thing that needs to be done is to examine and explore the data. This involves performing some initial checks to gain a better understanding of the structure, contents, and quality of the DataFrame.
One of the first things that can be done is to print out the first few rows of the DataFrame using the .head() method to get an idea of the data’s contents. This method shows the first five rows by default but a different number of rows can be specified by passing an integer argument to the method.
Next, the shape of the DataFrame can be checked using the .shape attribute, which gives the number of rows and columns. This can help in understanding the size of the data.
It may also be necessary to check the data types of the columns using the .dtypes attribute, which provides information on the type of data stored in each column. This can be helpful in identifying any inconsistencies or data type errors.
It is also good practice to check for missing values using the .isnull() method, which returns a DataFrame of Boolean values indicating whether each element is null or not. The .sum() method can then be used to get a count of missing values in each column.
Finally, exploratory data analysis can be performed to gain insights into the data. This involves using methods such as .describe(), .value_counts(), and .groupby() to summarize the data and identify patterns, trends, and anomalies.
By performing these initial checks and exploring the data, a better understanding of the DataFrame can be gained and any issues that need to be addressed can be identified before proceeding with further analysis.