Showing posts with the label Data Analyst

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How to Check Column Nulls and Replace: Pandas

Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda

These 10 Skills You Need to Become Data Analyst

To become a data analyst with Python, there are several technical skills you need to learn. Here are the key ones: #1 Python Programming Python is widely used in data analysis due to its simplicity, versatility, and the availability of powerful libraries. You should have a strong understanding of Python fundamentals, including data types, variables, loops, conditional statements, functions, and file handling. #2 Data Manipulation Libraries Familiarize yourself with libraries like NumPy and Pandas, which are essential for data manipulation and analysis. NumPy provides support for efficient numerical operations, while Pandas offers data structures (e.g., DataFrames) for easy data manipulation, cleaning, and transformation. #3 Data Visualization Gain proficiency in data visualization libraries like Matplotlib and Seaborn. These libraries enable you to create insightful visual representations of data, such as line plots, scatter plots, bar charts, histograms, and heatmaps. #4 SQL (Structu