Pandas Series Tutorial with Examples

1. Pandas Series Introduction

This is a beginner’s guide of Python pandas Series Tutorial where you will learn what is pandas Series? its features, advantages, and how to use panda Series with sample examples.

Every sample example explained in this tutorial is tested in our development environment and is available for reference.

All pandas Series examples provided in this tutorial are basic, simple, and easy to practice for beginners who are enthusiastic to learn pandas and advance their career in Data Science, analytics, and Machine Learning.

Note: In case you can’t find the pandas Series examples you are looking for on this tutorial page, I would recommend using the Search option from the menu bar to find your tutorial and sample example code, there are hundreds of tutorials in pandas on this website you can learn from.

2. What is Pandas Series

pandas Series is a one-dimensional array that is capable of storing various data types (integer, string, float, python objects, etc.). We can easily convert the list, tuple, and dictionary into Series using the Series() method. In pandas Series, the row labels of Series are called the index. The Series can have only one column. A List, NumPy Array, Dict can be turned into a pandas Series.   

3. Pandas Series vs DataFrame?

• As I explained above, pandas Series is a one-dimensional labeled array of the same data type whereas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. 
• In a DataFrame, each column of data is represented as a pandas Series.
• DataFrame column can have a name/label but, Series cannot have a column name.
• DataFrame can also be converted to Series and single or multiple Series can be converted to a DataFrame. Refer to pandas DataFrame Tutorial for more details and examples on DataFrame.

4. Pandas.series() Constructor

Below is the syntax of pandas Series Constructor, which is used to create Pandas Series objects.

# Pandas Series Constructor Syntax
Pandas.series(data,index,dtype,copy)

• data: The data contains ndarray, list, constants.
• Index: The index must be unique and hashable. np.arrange(n) if no index is passed.
• dtype: dtype is also a data type.
• copy: It is used to copy the data. The data contains ndarray, list, constants.

5. Create pandas Series

pandas Series can be created in multiple ways, From array, list, dict, and from existing DataFrame.

5.1 Create Series using array

Before creating a Series, first, we have to import the NumPy module and use array() function in the program. If the data is ndarray, then the passed index should be in the same length, if the index is not passed the default value is range(n).

# Create Series from array
import pandas as pd 
import numpy as np
data = np.array(['python','php','java'])
series = pd.Series(data)
print (series)

Yields below output. Notice that the column doesn’t have a name. And Series also adds an incremental sequence number as Index (first column) by default.

# Output:
0    python
1       php
2      java
dtype: object

Now, let’s see how to create a pandas DataFrame with a custom Index. To do so, will use index param which takes a list of index values. Make sure the index list matches the data size.

# Create pandas DataFrame with custom index
s2=pd.Series(data=data, index=['r1', 'r2', 'r3'])
print(s2)

Yields below output.

# Output:
r1    python
r2       php
r3      java
dtype: object

5.2 Create Series using Dict

A Dict can be used as input. Keys from Dict are used as Index and values are used as a column.

# Create a Dict from a input
data = {'Courses' :"pandas", 'Fees' : 20000, 'Duration' : "30days"}
s2 = pd.Series(data)
print (s2)

Yields below output.

# Output:
Courses     pandas
Fees         20000
Duration    30days
dtype: object

Now let’s see how to ignore Index from Dict and add the Index while creating a Series with Dict.

# To See index from Dict and add index while creating a Series.
data = {'Courses' :"pandas", 'Fees' : 20000, 'Duration' : "30days"}
s2 = pd.Series(data, index=['Courses','Course_Fee','Course_Duration'])
print (s2)

5.3 Create Series using List

Below is an example of creating DataFrame from List.

# Creating DataFrame from List
data = ['python','php','java']
s2 = pd.Series(data, index=['r1', 'r2','r3'])
print(s2)

Yields below output.

# Output:
r1 python
r2 php
r3 java
dtype:object

5.4 Create Empty Series

Sometimes you would require to create an empty Series. you can do so by using its empty constructor.

# Create empty Series
import pandas as pd
s = pd.Series()
print(s)

This shows an empty series.

6. Convert a Series into a DataFrame?

To convert Series into DataFrame, you can use pandas.concat(), pandas.merge(), DataFrame.join(). Below I have explained using concat() function. For others, please refer to pandas combine two Series to DataFrame

# Convert series to dataframe
courses = pd.Series(["Spark","PySpark","Hadoop"], name='courses')
fees = pd.Series([22000,25000,23000], name='fees')
df=pd.concat([courses,fees],axis=1)
print(df)

Yields below output.

# Output:
   courses   fees
0    Spark  22000
1  PySpark  25000
2   Hadoop  23000

7. Convert pandas DataFrame to Series?

In this section of the pandas Series Tutorial, I will explain different ways to convert DataFrame to Series. As I explained in the beginning, every column in a DataFrame is a Series. hence, we can convert single or multiple columns into Series.

1. Single DataFrame column into a Series (from a single-column DataFrame)
2. Specific DataFrame column into a Series (from a multi-column DataFrame)
3. Single row in the DataFrame into a Series

7.1 Convert a single DataFrame column into a series:

Let’s create a DataFrame with a single column. By using DataFrame.squeeze() to convert the DataFrame into a Series:

# Create DataFrame with single column
data =  ["Python","PHP","Java"]
df = pd.DataFrame(data, columns = ['Courses'])
my_series = df.squeeze()
print(my_series)
print (type(my_series))

The DataFrame will now get converted into a Series:

# Output:
0    Python
1       PHP
2      Java
Name: Courses, dtype: object
<class 'pandas.core.series.Series'>

7.2. Convert a specific DataFrame column into a series:

If you have a DataFrame with multiple columns, and you’d like to convert a specific column into a series.

For example, if we a multiple-column DataFrame

# Create DataFrame with multiple columns
import pandas as pd
data = {'Courses': ['Spark', 'PySpark', 'Python'],
        'Duration':['30 days', '40 days', '50 days'],
        'Fee':[20000, 25000, 26000]
        }
df = pd.DataFrame(data, columns = ['Courses', 'Duration', 'Fee'])
print(df)
print (type(df))

Let’s convert the Fee column into a Series.

# Pandas DataFrame column to series
my_series= df['Fee'].squeeze()

7.3 Convert DataFrame Row into a Series

Above, we have seen converting DataFrame columns into Series, here, I will explain converting rows into Series.

# Convert DataFrame row to series
my_series = df.iloc[2].squeeze()
print(my_series)
print (type(my_series))

Then, we can get the following series:

# Output:
Courses      Python
Duration    50 days
Fee             NaN
Name: 2, dtype: object
<class 'pandas.core.series.Series'>

8. Merge DataFrame and Series?

1. Construct a dataframe from the series.
2. After that merge with the dataframe.
3. Specify the data as the values, multiply them by the length, set the columns to the index and set params for left_index and set the right_index to True.

# Syntax for merge with the DataFrame.
df.merge(pd.DataFrame(data = [s.values] * len(s), columns = s.index), left_index=True, right_index=True)

9. Pandas Series Attributes:

10. Pandas Series Methods:

Continue..

Conclusion

In this pandas Series tutorial, we have learned about what is panda series? how to create a Panda Series with different types of inputs, convert pandas Series to DataFrame, and vice versa with working examples.

References

• https://pandas.pydata.org/docs/reference/api/pandas.Series.html
• https://www.w3schools.com/python/pandas/pandas_series.asp
• https://towardsdatascience.com/a-practical-introduction-to-pandas-series-9915521cdc69

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