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To concatenate pandas DataFrames vertically, you can use the concat function with axis=0. This will stack the DataFrames on top of each other.
To concatenate pandas DataFrames horizontally, you can use the concat function with axis=1. This will merge the DataFrames side by side.
Keep in mind that the DataFrames must have the same columns when concatenating horizontally, and the same index when concatenating vertically.
How to concatenate DataFrames using the append function in pandas?
To concatenate DataFrames using the append function in pandas, you can use the following syntax:
new_df = df1.append(df2)
Where df1 and df2 are the DataFrames you want to concatenate. The append() function will concatenate df2 to df1 and return a new DataFrame new_df.
You can also concatenate multiple DataFrames in one go by passing a list of DataFrames to the append() function like this:
new_df = df1.append([df2, df3, df4])
This will concatenate df2, df3, and df4 to df1 and return a new DataFrame new_df.
Note that the append() function does not modify the original DataFrames, instead it returns a new concatenated DataFrame.
How to concatenate DataFrames using the merge function in pandas?
To concatenate DataFrames using the merge function in pandas, you can follow these steps:
- Import the pandas library:
import pandas as pd
- Create two DataFrames to concatenate:
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2'], 'B': ['B0', 'B1', 'B2'], 'key': ['K0', 'K1', 'K2']})
df2 = pd.DataFrame({'C': ['C0', 'C1', 'C2'], 'D': ['D0', 'D1', 'D2'], 'key': ['K0', 'K1', 'K2']})
- Use the merge function to concatenate the DataFrames based on a common key:
merged_df = pd.merge(df1, df2, on='key')
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The resulting DataFrame will have columns from both input DataFrames that have the same key value:
A B key C D 0 A0 B0 K0 C0 D0 1 A1 B1 K1 C1 D1 2 A2 B2 K2 C2 D2
You can also specify different merge options like 'how' (inner, outer, left, right) and 'suffixes' for overlapping column names in the two DataFrames.
How to concatenate DataFrames using the join function in pandas?
You can concatenate DataFrames using the join function in Pandas by specifying the axis along which to join the DataFrames (axis=0 for rows, axis=1 for columns) and the type of join to perform (inner, outer, left, or right). Here's an example of how to concatenate DataFrames using the join function:
import pandas as pd
Create two sample DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']}) df2 = pd.DataFrame({'C': [4, 5, 6], 'D': ['d', 'e', 'f']})
Concatenate DataFrames along columns using the join function
result = df1.join(df2)
print(result)
This will output:
A B C D 0 1 a 4 d 1 2 b 5 e 2 3 c 6 f
In this example, the join function concatenated the DataFrames along columns by aligning the indices of the DataFrames before combining them. You can specify the type of join to perform by using the how parameter in the join function, like this:
result = df1.join(df2, how='outer')
This will perform an outer join, which includes all rows from both DataFrames, filling in missing values with NaN if necessary.