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Importing Finance Data

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Date and Time in Python

Python provides a module named datetime to deal with dates and times.

It allows you to set date ,time or both date and time using the date(),time()and datetime() functions respectively, after importing the datetime module .

import datetime feb_16_2019 =, month=2, day=16) feb_16_2019 =, 2, 16) print(feb_16_2019) #2019-02-16 time_13_48min_5sec = datetime.time(hour=13, minute=48, second=5) time_13_48min_5sec = datetime.time(13, 48, 5) print(time_13_48min_5sec) #13:48:05 timestamp= datetime.datetime(year=2019, month=2, day=16, hour=13, minute=48, second=5) timestamp = datetime.datetime(2019, 2, 16, 13, 48, 5) print (timestamp) #2019-01-02 13:48:05

Pandas DataFrame creation

The fundamental Pandas object is called a DataFrame. It is a 2-dimensional size-mutable, potentially heterogeneous, tabular data structure.

A DataFrame can be created multiple ways. It can be created by passing in a dictionary or a list of lists to the pd.DataFrame() method, or by reading data from a CSV file.

# Ways of creating a Pandas DataFrame # Passing in a dictionary: data = {'name':['Anthony', 'Maria'], 'age':[30, 28]} df = pd.DataFrame(data) # Passing in a list of lists: data = [['Tom', 20], ['Jack', 30], ['Meera', 25]] df = pd.DataFrame(data, columns = ['Name', 'Age']) # Reading data from a csv file: df = pd.read_csv('students.csv')

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Analyze Financial Data with Python

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