How to Reshape Pandas Series?

We can reshape the pandas series by using series.values.reshape() function. This reshape() function takes the dimension you wanted to reshape to. Note that this literally doesn’t reshare the Series instead, it reshapes the output of Series.values which is a NumPy Ndarray.

Before going to know the usage of reshape() we need to know about shape(), which is the number of elements in each dimension. Reshaping allows us to add or remove dimensions in an array. Reshape allows us to change the number of elements in each dimension.

1. Quick Examples of Reshap Pandas Series

If you are in a hurry, below are some quick examples of how to reshape the Series.


# Below are a quick example

# Example 1:apply reshape() function to pandas series
ser2 = ser.values.reshape((2,3))
  
# Example 2: use reshape pandas series
arr = ser.values
ser2 = arr.reshape((2, 3))

# Example 3: get reshape series using array.reshape() function
array = ['Spark','PySpark','Pandas','NumPy','Python',"Oracle"]
ser = pd.Series(array)
arr = ser.values
ser2 = arr.reshape((3, 2))

2. Syntax of Pandas Series reshape()

Following is the syntax of the Pandas Series reshape() function.


# Syntax of reshape()
Pandas.Series.values.reshape((dimension))

It returns an ndarray along with a specified shape.

Now, let’s create pandas series using a list of values.


import pandas as pd

# Create a Series
ser = pd.Series([2, 5, 8, 3, 6, 9])
print(ser)

4. Apply Reshape() Function to Pandas Series

Use series.values.reshape() function we can change the dimensions of the array. Here I will apply reshape() function to pandas series it will return the specified shape array.


# apply reshape() function to pandas series
ser2 = ser.values.reshape((2,3))
print(ser2) 
  
# use reshape pandas series
arr = ser.values
ser2 = arr.reshape((2, 3))
print(ser2)

Yields below output.


# Output
[[2 5 8]
 [3 6 9]]

5. Another Example to Reshape Series

Here I will create pandas series using array, then apply reshape() function to given pandas series, it will return the specified shape of the array.


# get reshape series using array.reshape() function
array = ['Spark','PySpark','Pandas','NumPy','Python',"Oracle"]
ser = pd.Series(array)
arr = ser.values
ser2 = arr.reshape((3, 2))
print(ser2)

Yields below output.


[['Spark' 'PySpark']
 ['Pandas' 'NumPy']
 ['Python' 'Oracle']]

6. Complete Example For Reshap Pandas Series


import pandas as pd

# Create a Series
ser = pd.Series([2, 5, 8, 3, 6, 9])
print(ser)

# apply reshape() function to pandas series
ser2 = ser.values.reshape((2,3))
print(ser2) 
  
# Use reshape pandas series
arr = ser.values
ser2 = arr.reshape((2, 3))
print(ser2)

# get reshape series using array.reshape() function
array = ['Spark','PySpark','Pandas','NumPy','Python',"Oracle"]
ser = pd.Series(array)
arr = ser.values
ser2 = arr.reshape((3, 2))
print(ser2)

7. Conclusion

In this article, I have explained how to reshape the pandas series using series.values.reshape(), array.reshape() functions with examples.

Happy Learning !!

References

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