Both are the most popular libraries for Data science and machine learning-related tasks.
Although their core objective is the same and both are equally used in various Python projects, putting together Data analysis tasks more leisurely.
pandas library works effectively for numeric, alphabets, and types of data simultaneously, as heterogeneous.
Whereas Numpy library works better with only numerical data, efficient storage, and fastly performs mathematical operations on array-based and matrix-based numeric values.
Major Differences
| Scale | Pandas | NumPy |
| Primary Aim | It is useful for data analysis tasks in Python. | It is useful when working with Numerical values. It makes it easy to apply mathematical functions. |
| Super Features | It comes with some tools for functions like series and data frames. | It puts all its strength into managing arrays and their related mathematical functions. |
| Built by | Wes McKinney in 2008 | Travis Oliphant in 2005 |
| Memory consumption | It takes more storage. It is not as useful in storing data as NumPy. | It consumes less amount of storage. It is useful when it comes to managing storage, |
| Core Object | It renders a 2d table object called DataFrame. | It gives a multidimensional array. |
| Popular, jobs | It is introduced in 73 company stacks and 48 developer stacks. | It is discussed in 61 company stacks and 34 developer stacks. |
What is pandas?
Pandas is a universal data analysis toolkit for Python. Its applications range from working numerical value, available data tables value, a,b,c.
Also, changing an array format into a table format is possible.
Worth including here. It is based on Numpy and written in several languages counting python, C, Cython.
When it comes to collecting data: It can fetch data from several formats. SQL, CSV, JSON formats are included.
What is numPy?
NumPy is a free Python library that comes up with tools for evaluating numerical data. It is significantly used to perform mathematical operations on statistical data.
The name NumPy is an abbreviation of Numerical Python.
Thus, it gives more value to numerical data, when working with multidimensional arrays ( Matrix), it makes it easier to perform scientific computing and mathematical operations.
Also read: Anaconda vs Python
which is better for data science?
Honestly speaking, there is no worst and best word when comparing both of them.
Both the Python libraries are equally popular and do their tasks accordingly in a convenient way.
However, in case you are seriously looking for drawbacks and advantages.
Then, that is to say, in terms of speed performance is slightly slower than NumPy when the number of rows is less than 500K, beyond that; its performance is well-appreciated.
On the other hand, the NumPy library basically does not give a better performance when the number of rows goes beyond 500k.
It is handy only in working with arrays and applying mathematical operations on them.
What pandas library can do?
It is getting popular as the most useful Python library in data science.
One of its handy work applications is that It provides an in-memory 2d table object, also called Dataframe.
That overview data is similar to a spreadsheet in such a format. It has columns and rows.
You can get an idea, how handy the data tables could be when working with data analysis.
You can plot a graph, computing matrix operations, store, and view the data in a more effective way.
We’ll walk through some of its super powerful tools that make this stand out.
They are just some basic applications; in reality, Data analysis is the name of playing with giant data, so picture huge while looking at the below operations.
To install it on your notebook; Spyder or PYcharm, run the following command in the console.
pip install pandas
If you see an error while installing the library, follow the video to install this library.
To import it into your program, add the following line in your code:
import pandas as pd
Examples:
Below are some examples showing how this python library is useful when working with data.
Series objects:
The Pandas series gives more power to us, handling mathematical functions.
By default, with this library, each row is assigned by a numeric value, with a base of 0.
However, you can control this indexing; hence you can use state index=false next to an array not to pick the indexing values.
A series can be created in Pandas using several inputs; Array, Dict, Scalar value, or constant.
import pandads as pd ser = pd.Series([0, 10, 20, 30, 40, 50]) print(ser) ## output: 0 0 1 10 2 20 3 30 4 40 5 50 dtype: int64
We can change the index values by putting a new value for index- such as ser = pd.series [(1, 2, 3)] ,index = [‘a’,’b’,’c’]), and we can also limit the number of results we want to have, by placing the print s[-2].
The result will only pick the last two values in this way.
Also read: Frameworks for Python
DataFrame objects:
We use Dataframe, a functionality when we have to work with data tables. a number of mathematical operations can be applied to them.
All in all, its DataFrame comes up with powerful functions to work in columns and rows.
We can easily manage rows, columns, and several mathematical operations.
Below is a simple workout of the Dataframe type.
import pandas as pd data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
print(df)
#output
Name Age
rank1 Tom 28
rank2 Jack 34
rank3 Steve 29
rank4 Ricky 42
Similarly, adding two or more columns turned out easier with this library.
import pandas as pd
d = {'one' : pd.Series([1, 2, 3], index=['a', 'b', 'c']),
... 'two' : pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])}
df = pd.DataFrame(d)
print ("Adding a column by passing as Series:")
df['three']=pd.Series([10,20,30],index=['a','b','c'])
print (df)
#output 1
Adding a column by passing as Series:
one two three
a 1.0 1 10.0
b 2.0 2 20.0
c 3.0 3 30.0
d NaN 4 NaN
print ("Adding a new column using the existing columns in DataFrame:")
df['four']=df['one']+df['three']
print (df)
#output 2
Adding a new column using the existing columns in DataFrame:
one two three four
a 1.0 1 10.0 11.0
b 2.0 2 20.0 22.0
c 3.0 3 30.0 33.0
d NaN 4 NaN NaN
Including these, several tools are out there in Pandas, that all make it stands out for data analysis.
What NumPy library can do?
It was significantly brought up for handling mathematical and logical operations on arrays. It is widely used among data scientists who have to work with numerical values, multidimensional preferably.
One of the key advantages of this python library is, it is aligned towards consuming low storage, faster, and easy to understand.
Overall, it made it more comfortable working with numeric values, adding, subtracting, algebraic operations, and so forth.
Below are a super quick introduction of some of its highly inevitable built-in functions.
First thing, get this library. Use this command-import numpy as np
Examples:
Below are some examples showing how the NumPy library is useful when working with data.
Filtering:
We can filter a numerical value quickly, below given an example.
import numpy as np arr_1 = np.array([1, 2, 3, 4, 5, 6]) fltr = [True, False, True, False, True, False] arr_2 = arr_1[fltr] print(arr_2) ## output [1 3 5]
Reshaping an array:
Often in a Data analysis task, reshaping a value becomes necessary; unlike Python logics, numPy comes up with some features that help in reshaping a value hassle-free.
arr_1 = np.array([1, 2, 3, 4, 5, 6]) arr_2 = arr_1.reshape(3, 2) print(arr_2) # reshaping an array ## output [[1 2] [3 4] [5 6]]
As you can see, we used a NumPy property to reshape a value. Otherwise, it will give output something like this;
Also read: Free Python course for Absolute Beginners
