Pandas Stack Two Series Vertically and Horizontally

pandas.concat() function is used to stack two given series vertically and horizontally in pandas. When you concat() two pandas Series along with row-wise, it creates a new Series where the elements are all rows of two Series, basically it does append one Series with another. In this article, I will explain how to stack two pandas series vertically and horizontally with examples.

Key Points –

• Pandas provides functionality to stack two Series vertically and horizontally using various functions.
• The pd.concat() function is commonly used to stack Series vertically by concatenating them along a specified axis.
• To stack Series horizontally, the pd.concat() function can also be used by specifying axis=1.
• The pd.DataFrame() constructor can stack two Series horizontally by passing them as columns in a dictionary.

1. Quick Examples of Stack Two Series Vertically and Horizontally

If you are in a hurry, below are some quick examples of how to stack two pandas Series vertically and horizontally.

# Below are a quick examples.

# Example 1: Stack two series horizontally 
# Using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 1)

# Example 2: Stack two series vertically
# Using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 0)

# Example 3: Stack two series vertically 
# Using Series.append() function
ser2 = ser.append(ser1)

# Example 4: Use pandas.concat() & ignore_index=True
ser2 = pd.concat([ser, ser1], ignore_index=True)

2. Create Two Pandas Series

Pandas Series is a one-dimensional, Index-labeled data structure available only in the Pandas library. It can store all the datatypes such as strings, integers, float, and other python objects. We can access each element in the Series with the help of corresponding default indices.

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

import pandas as pd
  
# Create two pandas series
ser = pd.Series(['Java','Spark','PySpark','Pandas','NumPy','Python',"Oracle"])
ser1 = pd.Series([20000,25000,23000,28000,55000,23000,28000])

print(ser)  
print(ser1)

Yields below output.

# Output:
0       Java
1      Spark
2    PySpark
3     Pandas
4      NumPy
5     Python
6     Oracle
dtype: object
0    20000
1    25000
2    23000
3    28000
4    55000
5    23000
6    28000
dtype: int64

3. Stack Two Pandas Series Horizontally

pandas.concat() function is used to stack two pandas Series horizontally. For that, we need to pass axis=1 along with a list of series. If you wanted to concatenate two pandas DataFrame columns refer pandas.concate() function.

# Stack two series horizontally 
# Using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 1)
print(ser2)

Yields below output.

# Output:
         0      1
0     Java  20000
1    Spark  25000
2  PySpark  23000
3   Pandas  28000
4    NumPy  55000
5   Python  23000
6   Oracle  28000

4. Stack Two Pandas Series Vertically

Alternatively, you can also stack two pandas Series vertically by passing axis=0 parameter into pandas.concat() function.

# Stack two series vertically using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 0)
print(ser2)

Yields below output.

# Output:
0       Java
1      Spark
2    PySpark
3     Pandas
4      NumPy
5     Python
6     Oracle
0      20000
1      25000
2      23000
3      28000
4      55000
5      23000
6      28000
dtype: object

5. Stack Two Series Vertically Using Series.append() Function

Similarly, you can also use Series.append() function to stack two series vertically. For E.x, ser.append(ser1) appends ser1 to the ser Series. If you want to append two pandas DataFrame refer to append() function.

# Stack two series append() vertically 
ser2 = ser.append(ser1)
print(ser2)

Yields the same output as above.

6. Use pandas.concat() & ignore_index=True

Using pandas.concat() with ignore_index=True to reset the index on the combined Series. For instance, pd.concat() vertically stacks ser and ser1, and the resulting Series ser2 has a new continuous index starting from 0, ignoring the original indices of the input Series.

# Use pandas.concat() & ignore_index=True
ser2 = pd.concat([ser, ser1], ignore_index=True)
print(ser2)

Yields below output.

# Output:
0        Java
1       Spark
2     PySpark
3      Pandas
4       NumPy
5      Python
6      Oracle
7       20000
8       25000
9       23000
10      28000
11      55000
12      23000
13      28000
dtype: object

7. Complete Examples

import pandas as pd

# Create the Series
ser = pd.Series(['Java','Spark','PySpark','Pandas','NumPy','Python',"Oracle"])
ser1 = pd.Series([20000,25000,23000,28000,55000,23000,28000])
print(ser)  
print(ser1)

# Stack two series horizontally 
# Using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 1)
print(ser2)

# Stack two series vertically 
# Using pandas.concat() function
ser2 = pd.concat([ser, ser1], axis = 0)
print(ser2)

# Stack two series vertically 
# Using Series.append() function
ser2 = ser.append(ser1)
print(ser2)

# Use pandas.concat() & ignore_index=True
ser2 = pd.concat([ser, ser1], ignore_index=True)
print(ser2)

Frequently Asked Questions on Stack Two Series Vertically and Horizontally

Conclusion

In this article, I have explained how to stack two given series vertically and horizontally in pandas using pandas.concat(), Series.append() functions with examples.

Happy Learning !!

Related Articles

• Get Series Index in Pandas
• Pandas Series.clip() Function
• Pandas Series iloc[] Function
• How to Append Pandas Series?
• How to Rename a Pandas Series
• Pandas series.str.get() Function 
• Pandas Series round() Function 
• Pandas Series.dtype() Function
• Pandas Series Drop duplicates() Function
• How to Make a Histogram in Pandas Series?
• Change the Index Order in Pandas Series
• Convert Series to Dictionary(Dict) in Pandas
• Convert Pandas Series to NumPy Array
• How to Convert NumPy Array to Pandas Series

References

• https://pandas.pydata.org/docs/reference/api/pandas.concat.html