An Overview of Working with Dataframes and Numpy Array in Python
Python is an excellent programming language that is widely used in data analysis and manipulation across various industries. As Python gains popularity, it has become increasingly vital to master data analysis frameworks like Pandas and Numpy.
These frameworks are essential in the creation and manipulation of data structures such as data frames and Numpy arrays. In this article, we delve into data frames and Numpy arrays in Python.
We will explore the creation of data frames using the Pandas library and the process of converting Pandas data frames to Numpy arrays. We will also look at how to change data types within a data frame, among other aspects of working with these essential data structures.
Data Frames in Python
A data frame is a two-dimensional structure that stores data values and is used for data analysis. It can store different data types and lengths within each column, making it an incredible tool for data manipulation.
Python has matured data manipulation libraries, and the most widely used is the Pandas library. Data frames are created using Pandas library, which is built on top of Numpy.
You can install both libraries using pip (Pythons package installer) on your command prompt or terminal by entering !pip install pandas and !pip install numpy, respectively. Creating
Data Frames in Python using Pandas
Pandas data frames can be created from various data sources, including CSV, Excel, databases, JSON, among others.
To create a data frame, you need to have a dataset, which can be a CSV, Excel file, a database, or even a manually created dataset. For example, consider the following code that creates a dataframe consisting of the top five countries with the highest number of COVID-19 cases as of October 2021:
First, we need to import the Pandas library, which well use around our codes:
import pandas as pdNow let’s create a new data frame and fill it up with data. python
# Create a dictionary with data we want to store in the DataFrame
data = {
'Country': ['USA', 'India', 'Brazil', 'Russia', 'France'],
'Number of cases': [45_767_463, 34_936_428, 22_928_293, 8_111_379, 7_161_054]
}
# Creating dataframe from dictionary we've just created
dataframe = pd.DataFrame(data)
# Output the dataframe
print(dataframe)
This should output:
Country Number of cases
0 USA 45767463
1 India 34936428
2 Brazil 22928293
3 Russia 8111379
4 France 7161054
As you can see, Pandas data frames are useful in organizing data. We can now manipulate and analyze the data with various data manipulation techniques offered by Pandas.
Converting Pandas Data Frames to Numpy Arrays
Often, its necessary to convert a Pandas data frame to a Numpy array so that further analysis can be conducted. Converting a Pandas data frame to a Numpy array can be done using the dataframe.to_numpy() method.
The to_numpy() method returns a Numpy array representation of the data frame. For example, consider the previous data frame of the top five countries with the highest number of COVID-19 cases.
import pandas as pd
data = {
'Country': ['USA', 'India', 'Brazil', 'Russia', 'France'],
'Number of cases': [45_767_463, 34_936_428, 22_928_293, 8_111_379, 7_161_054]
}
df = pd.DataFrame(data)
ndarray = df.to_numpy()
print(ndarray)
The output is:
array([['USA', 45767463],
['India', 34936428],
['Brazil', 22928293],
['Russia', 8111379],
['France', 7161054]], dtype=object)
You can see that the data frame has been converted to Numpy array. Its important to note that when the data frame contains different data types, Numpy converts them all to a single data type, in this case, an object.
Changing Data Types within a Data Frame
Sometimes data in a column may be stored in the wrong data type. For example, a column storing numerical data may be stored as a string data type.
The datatype can be changed by using the dataframe.astype() method.
# creating the staff dataframe
import pandas as pd
staff = pd.DataFrame(
{'Name': ['John Smith', 'Jane Doe', 'Joe Schmoe'],
'Age': [37, 29, 47],
'Salary': ['100,000', '80,000', '115,000']})
# describe the dataframe's datatypes
print(staff.dtypes)
# change datatype from string to int64
staff['Salary'] = staff['Salary'].astype('float')
print(staff.dtypes)
The output looks like this:
Name object
Age int64
Salary object
dtype: object
Name object
Age int64
Salary float64
dtype: object
As you can see, the Salary column that was initially of a string data type has been changed to a float data type.
Conclusion
In this article, we have explored the basics of working with data frames and Numpy arrays in Python. We have discussed the creation of data frames using the Pandas library and seen how to convert Pandas data frames to Numpy arrays.
We have also examined how to change the data types within a data frame. Data frames and Numpy arrays are essential tools for data analysis and manipulation in Python.
Having a deep understanding of these data structures will increase your proficiency and productivity in data analysis. We hope this article has proved insightful in your Python journey.
3) Converting Numpy Arrays to Pandas Dataframes
Numpy arrays are popular in scientific computing and numerical analysis because of their performance advantages. However, Pandas data frames provide more versatility in data manipulation, and thus it is essential to be able to convert Numpy arrays to Pandas data frames.
In this section, we will explore how to accomplish this task.
Defining a Numpy Array
A Numpy array represents a multidimensional, homogeneous collection of data values. It can be created using various methods, including using the numpy.array() function or using the Numpys built-in functions like numpy.zeros(), numpy.ones(), and numpy.random.rand(), among others.
Consider the following example:
import numpy as np
# create a 2D numpy array
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
With this, we have created a two-dimensional Numpy array with three rows and three columns. Conversion of Numpy array to Pandas Dataframe using pandas.DataFrame() function
To convert a Numpy array to a Pandas data frame, we use the pandas.DataFrame() function, which accepts a Numpy array as an argument.
import pandas as pd
# create a Pandas dataframe
df = pd.DataFrame(arr)
With this, we have created a Pandas data frame from the Numpy array.
Providing Headers to Rows and Columns in the Converted Dataframe
The pandas.DataFrame() function automatically infers the column names and row indices. However, it is prudent always to provide descriptive column and row names to improve readability and develop clearer documentation.
# create a Pandas dataframe with custom column names and row indices
df_custom = pd.DataFrame(arr, columns=['a', 'b', 'c'], index=['x', 'y', 'z'])
Here, we have created a Pandas data frame with custom column names and row indices. Note that it is possible to assign column and index names to the original Numpy array before converting it into a Pandas data frame.
# assign column and index names to the Numpy array before creating the Pandas dataframe
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
column_names = ['a', 'b', 'c']
index_names = ['x', 'y', 'z']
arr_df = pd.DataFrame(data=arr, columns=column_names, index=index_names)
This will result in a Pandas data frame with designated column and index names. In summary, converting a Numpy array to a Pandas data frame is an essential process in data analysis as Pandas data frames provide versatile tools for data manipulation.
The process can be achieved using Pandas DataFrame() function, which accepts a Numpy array as an input argument. Additionally, it is recommended to provide descriptive column and row names to improve data readability and documentation.
4)
Conclusion
In conclusion, we have explored the basics of working with data frames and Numpy arrays in Python. We have discussed the creation of data frames using the Pandas library and seen how to convert Pandas data frames to Numpy arrays.
We have also examined how to change the data types within a data frame. Furthermore, we have delved into converting Numpy arrays to Pandas data frames, discussing how to define a Numpy array and convert it into a Pandas data frame.
We have also seen how to provide headers for rows and columns in the converted data frame. Understanding these fundamentals will increase your proficiency and productivity in data analysis, leading to better and more informed decision-making processes.
In summary, this article has covered the fundamental concepts of working with data frames and Numpy arrays in Python, outlining the creation of data frames using the Pandas library, conversion of Pandas data frames to Numpy arrays, and changing data types within a data frame. We have also discussed how to convert a Numpy array to a Pandas data frame, emphasizing the importance of providing headers for rows and columns.
These fundamental concepts are essential in data analysis and form a foundation for more advanced techniques. By mastering these concepts, you can improve your proficiency and productivity in data analysis, leading to better decision-making processes.
Remember to always provide descriptive column and row names to improve data readability and documentation.