How can you use Python to clean and transform your data?

As a business analyst, use Python's 'pandas' library for data cleaning and transformation. Import data using 'read_csv()' or 'read_excel()', handling missing values with 'fillna()' or 'dropna()'. Clean duplicates with 'drop_duplicates()', and rename columns with 'rename()'. Remove unnecessary columns using 'drop()' and apply string cleaning with methods like 'strip()' and 'replace()'. For transformation, convert date columns with 'pd.to_datetime()' and extract components. Create new features using 'apply()' or 'map()'. Utilize 'groupby()' for aggregations and gain insights. Clip outliers with 'clip()' or use 'quantile()' for robust handling. Export data using 'to_csv()' or 'to_excel()', ensuring proper index handling.

Before importing data, be sure to understand requirements and assess whether you need the entire dataset or only a sample of 10,000 - 100,000 records or so. Importing a large dataset of millions of records will affect performance and run time. 'Pandas-profiling' allows for a quick overview of the dataset for explanatory analysis, and is great for exploring new datasets from unfamiliar sources. Dask is similar to Pandas library but is better for handling larger-than-memory datasets with distributed computing if utilizing multiple cloud VMs.

Clean and transform data in Python using Pandas and NumPy. Load data into a DataFrame with Pandas. Handle missing values by dropping rows or filling with mean. Remove duplicates and standardize data formats. Normalize numerical data using Min-Max scaling or Z-score. Encode categorical variables with one-hot encoding. Perform feature engineering and parse date and time data. Preprocess text data using regular expressions. Handle outliers using statistical methods. Aggregate and group data with Pandas' groupby. These techniques enable effective data preparation for analysis or machine learning, ensuring data quality and usability.

As an IT business analyst, leveraging Python's pandas library is crucial for efficient data cleaning. Identify missing values using 'isnull()' and visualize them with descriptive statistics. Decide whether to drop missing values using 'dropna()' or fill them using 'fillna()' based on the context. For invalid values, use methods like 'replace()' for specific replacements or 'to_numeric()' to convert columns to numeric, handling errors gracefully. Document your decisions to maintain transparency and aid future analyses. Consistently validate data against business rules, ensuring conformity and reliability. This proactive approach ensures that data quality aligns with business goals and enhances decision-making processes.

I believe there is an art in handling missing and invalid values. I suggest first getting a count or percentage of missing values in your dataset by column: df.isnull().sum().sort_values(ascending = False)/len(df) From this, assess whether you should 1) fill them 2) replace them with other values 3) drop them. I tend to approach data conservatively in that if you’re working on a small dataset and also need to later train the data, then perhaps the third option of removing data is too extreme – resulting a substantial loss of data. Some values can be replaced with outliers, such as 999, that can be filtered out in the analysis and reporting step.

Dataset with too many outliers should be standardized and normalized. In my experience, working with public health and survey data, there is a lot of "No Answer", "Refused to Answer", or "Don't Know" to questions related to certain variables. Normalizing all these into one type of answer will improve data analysis. My favorite go-to is Python .map() function for this.

As an IT business analyst, use Python's pandas to standardize and normalize data. Standardization ensures a common format (e.g., dates, currencies), while normalization maintains a consistent range for better comparison. Collaborate on rules with domain experts, document transformations, and prioritize alignment with business needs. This approach fosters reliable analysis, aiding informed decision-making and operational efficiency.

I find that choosing the right export format is crucial. Consider the end-users and their preferences – whether it's a client who prefers Excel for easy viewing or a developer who needs JSON for seamless integration. Tailoring the export format enhances the usability of the results and facilitates smoother collaboration across diverse stakeholders.