The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with arrays. Whereas, Pandas library is built on NumPy and provides easy-to-use data structures and data analysis tools for the Python programming language.
Installing
Numpy - pip install numpy
Pandas - pip install pandas
NumPy:
- creating array
- a = np.array([1,2,3])
- Create an array of zeros
- np.zeros((3,4))
- Create an array with random values
- np.random.random((2,2))
- Create an empty array
- np.empty((3,2))
- Transposing Array
- i = np.transpose(b)
- Append items to an array
- np.append(h,g)
- Delete items from an array
- np.delete(a,[1])Pandas:
- Series
- s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd'])
- DataFrame
- data = {'Country': ['Belgium', 'India', 'Brazil'], 'Capital': ['Brussels', 'New Delhi', 'Brasília'],'Population': [11190846, 1303171035, 207847528]}
- df = pd.DataFrame(data,columns=['Country', 'Capital', 'Population'])
- Read and Write to CSV
- pd.read_csv('file.csv', header=None, nrows=5)
- df.to_csv('myDataFrame.csv')
- Read and Write to Excel
- pd.read_excel('file.xlsx')
- pd.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1')
- Info on DataFrame
- df.info()