![]() When concatenating vertically, the dataframes are stacked on top of each other and when concatenating horizontally, any missing values are filled with `NaN`. The `concat()` function in Pandas can be used to combine two dataframes vertically or horizontally. In most cases, you join two data frames by one or more common key variables (i.e. Joining DataFrames in this way is often useful when one DataFrame is a lookup table containing additional data that we want to include in the other. To merge two data frames (datasets) horizontally, use the merge function. The columns containing the common values are called join key (s). Note that when concatenating horizontally, any missing values are filled with `NaN`. Combining DataFrames using a common field is called joining. # concatenate the two dataframes horizontally (side by side) Note that by default, `concat()` concatenates the dataframes vertically (on top of each other), but you can also concatenate them horizontally (side by side) by setting the `axis` parameter to 1: Use append: dfmerged df1.append (df2, ignoreindexTrue) And to keep their indexes, set ignoreindexFalse. # concatenate the two dataframes vertically (stacked on top of each other) 9 Answers Sorted by: 251 DEPRECATED: DataFrame.append and Series.append were deprecated in v1.4.0. To combine two dataframes in Pandas, you can use the `concat()` function. We’ll go through an example of concatenating vertically (stacked on top of each other) and horizontally (side by side), as well as discuss what happens when there are missing values. The different arguments to merge() allow you to perform natural join. In this blog post, we will look at how to combine two dataframes in Pandas using the `concat()` function. We can Join or merge two data frames in pandas python by using the merge() function. ![]()
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