Python Pandas library Tutorial PART-6| Pandas library in python|Datascience|CodeWithTuqi
In this video, we will study about #concatenation and merging of #DataFrames in the #Python #Pandas library
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Pandas is a highly-efficient and widely used data analysis tool. The core data structure of Pandas is dataframe which represents data in tabular form with labeled rows and columns. There are many powerful and flexible functions and methods of DataFrame that ease and expedite the data cleaning and analysis process.
Data science projects usually require us to gather data from different sources. Hence, as part of data preparation, we may need to combine dataframes. Both concat and merge functions are used to combine dataframes. In this post, I will explain 3 key differences between them.
1. The Way of Combining
Concat function concatenates dataframes along rows or columns. We can think of it as stacking up multiple dataframes.
Merge combines dataframes based on values in shared columns. Merge function offers more flexibility compared to concat function because it allows combinations based on a condition.
2. Axis parameter
3. Join vs How
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