• Descriptive Statistics
  • Data Visualization with Pandas
  • Handling Missing Data
  • Working with Dates and Times
  • Merging and Joining DataFrames

Working with Social Media Data in Pandas

Social media data is a valuable source of information for businesses, researchers, and individuals. It can be used to track trends, understand customer sentiment, and identify influencers. However, social media data can be difficult to work with, as it is often unstructured and noisy.

Pandas is a powerful Python library that can be used to handle social media data. Pandas provides a number of features that make it well-suited for working with social media data, including:

  • DataFrames:┬áPandas DataFrames are a powerful way to store and manipulate structured data. DataFrames can be used to store social media data such as tweets, posts, and comments.
  • Time series analysis:┬áPandas provides a number of tools for working with time series data. This can be useful for analyzing social media data that is collected over time.
  • Text analysis:┬áPandas provides a number of tools for working with text data. This can be useful for analyzing social media data that contains text such as tweets, posts, and comments.

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Data Munging with Pandas

Data munging is a crucial process for any data analyst. Data wrangling is often a time-consuming and repetitive task, but it is essential to ensure that the data is accurate and reliable. Data munging is the process of cleaning, transforming, formatting, and combining raw data into a meaningful format suitable for further analysis and modeling.

We will explore the process of data munging with the Pandas library. Pandas is a Python library designed for data manipulation and analysis. It provides a high-level interface to data structures such as Series and DataFrames, making it easy to work with large datasets. (more…)

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How to Get Average Across Columns in Pandas

To calculate the average across columns in pandas, you can use the mean method on a DataFrame object. The mean method returns the mean of the values over the requested axis. By default, the axis is 0, which means the mean is calculated along the index (row) axis. If you want to calculate the mean along the column axis, you can specify axis=1 as an argument. (more…)

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Pandas Data validation

Data validation is an essential step in any data analysis or machine learning project. It involves checking data quality, consistency, and correctness to ensure that the data is reliable and suitable for the intended analysis or modeling. Pandas provides several functions and tools for data validation, such as checking for missing values, checking for duplicates, checking data types, and more. Here are some common data validation tasks in Pandas: (more…)

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