NumPy and Basic Pandas | Introduction To Financial Python on QuantConnect

# NumPy and Basic Pandas | Introduction To Financial Python on QuantConnect > Source: https://www.quantconnect.com/learning/articles/introduction-to-financial-python/numpy-and-basic-pandas NumPy and Basic Pandas | Introduction To Financial Python on QuantConnect Pricing Data Community Algorithm Lab Documentation Sign In learning center articles / Introduction To Financial Python NumPy and Basic Pandas 4/14 Author Jing Wu 2018-06-04 Introduction Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas. NumPy NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data. Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package: import numpy as np Basic NumPy Arrays A NumPy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. Here we make an array by passing a list of Apple stock prices: price_list = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62] price_array = np.array(price_list) print(f'{price_array} {type(price_array)}') [out]: [ 143.73 145.83 143.68 144.02 143.5 142.62] <class 'numpy.ndarray'> Notice that the type of array is "ndarray" which is a multi-dimensional array. If we pass np.array() a list of lists, it will create a 2-dimensional array. Ar = np.array([[1,3], [2,4]]) print(f'{Ar} {type(Ar)}') [out]: [[1 3] [2 4]] <class 'numpy.ndarray'> We get the dimensions of an ndarray using the .shape attribute: print(Ar.shape) [out]: (2, 2) If we create an 2-dimensional array (i.e. matrix), each row can be accessed by index: print(Ar[0]) [out]: [1 3] print(Ar[1]) [out]: [2 4] If we want to access the matrix by column instead: print(f'First column: {Ar[:,0]}') [out]: First column: [1 2] print(f'Second column: {Ar[:,1]}') [out]: Second column: [3 4] Array Functions Some functions built in NumPy that allow us to perform calculations on arrays. For example, we can apply the natural logarithm to each element of an array: np.log(price_array) [out]: [4.96793654 4.98244156 4.9675886 4.96995218 4.96633504 4.96018375] Other functions return a single value: np.mean(price_array) [out]: 143.896666667 print(np.std(price_array)) [out]: 0.967379047852 print(np.sum(price_array)) [out]: 863.38 print(np.max(price_array)) [out]: 145.83 The functions above return the mean, standard deviation, total and maximum value of an array. Pandas Pandas is one of the most powerful tools for dealing with financial data. First we need to import Pandas: import pandas as pd Series Series is a one-dimensional labeled array capable of holding any data type (integers, strings, float, Python object, etc.) We create a Series by calling pd.Series(data), where data can be a dictionary, an array or just a scalar value. price = [143.73, 145.83, 143.68, 144.02, 143.5, 142.62] s = pd.Series(price) print(s) 0 143.73 1 145.83 2 143.68 3 144.02 4 143.50 5 142.62 We can customize the indices of a new Series: s = pd.Series(price, index = ['a', 'b', 'c', 'd', 'e', 'f']) print(s) a 143.73 b 145.83 c 143.68 d 144.02 e 143.50 f 142.62 Or we can change the indices of an existing Series: s.index = [6,5,4,3,2,1] print(s) 6 143.73 5 145.83 4 143.68 3 144.02 2 143.50 1 142.62 Series is like a list since it can be sliced by index: print(s[1:]) print(s[:-2]) 5 145.83 4 143.68 3 144.02 2 143.50 1 142.62 dtype: float64 6 143.73 5 145.83 4 143.68 3 144.02 dtype: float64 Series is also like a dictionary whose values can be set or fetched by index label: print(s[4]) s[4] = 0 print(s) 143.68 6 143.73 5 145.83 4 0.00 3 144.02 2 143.50 1 142.62 dtype: float64 Series can also have a name attribute, which will be used when we make up a Pandas DataFrame using several series. s = pd.Series(price, name = 'Apple Prices') print(s) print(s.name) 0 143.73 1 145.83 2 143.68 3 144.02 4 143.50 5 142.62 Name: Apple Prices, dtype: float64 Apple Prices We can get the statistical summaries of a Series: print(s.describe()) count 6.000000 mean 143.896667 std 1.059711 min 142.620000 25% 143.545000 50% 143.705000 75% 143.947500 max 145.830000 Time Index Pandas has a built-in function specifically for creating date indices: pd.date_range(). We use it to create a new index for our Series: time_index = pd.date_range('2017-01-01', periods = len(s), freq = 'D') print(time_index) s.index = time_index print(s) DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06'], dtype='datetime64[ns]', freq='D') 2017-01-01 143.73 2017-01-02 145.83 2017-01-03 143.68 2017-01-04 144.02 2017-01-05 143.50 2017-01-06 142.62 Freq: D, Name: Apple Prices, dtype: float64 Series are usually accessed using the iloc[] and loc[] methods. iloc[] is used to access elements by integer index, and loc[] is used to access the index of the series. iloc[] is necessary when the index of a series are integers, take our previous defined series as example: s.index = [6,5,4,3,2,1] print(s) print(s[1]) 6 143.73 5 145.83 4 143.68 3 144.02 2 143.50 1 142.62 Name: Apple Prices, dtype: float64 142.62 If we intended to take the second element of the series, we would make a mistake here, because the index are integers. In order to access to the element we want, we use iloc[] here: print(s.iloc[1]) [out]: 145.83 While working with time series data, we often use time as the index. Pandas provides us with various methods to access the data by time index. s.index = time_index print(s['2017-01-03']) [out]: 143.68 We can even access to a range of dates: print(s['2017-01-02':'2017-01-05']) 2017-01-02 145.83 2017-01-03 143.68 2017-01-04 144.02 2017-01-05 143.50 Freq: D, Name: Apple Prices, dtype: float64 Series[] provides us a very flexible way to index data. We can add any condition in the square brackets: print(s[s < np.mean(s)]) print(s[(s > np.mean(s)) & (s < np.mean(s) + 1.64*np.std(s))]) 2017-01-01 143.73 2017-01-03 143.68 2017-01-05 143.50 2017-01-06 142.62 Name: Apple Prices, dtype: float64 2017-01-04 144.02 Freq: D, Name: Apple Price List, dtype: float64 As demonstrated, we can use logical operators like & (and), | (or) and ~ (not) to group multiple conditions. Summary Here we have introduced NumPy and Pand as for scientific computing in Python. In the next chapter, we will dive into Pandas to learn resampling and manipulating Pandas DataFrame, which are commonly used in financial data analysis. Try the world leading quantitative analysis platform today Sign Up Previous: Python: Functions and Object-Oriented Programming Next: Pandas: Resampling and DataFrame ON THIS PAGE Introduction NumPy Pandas Summary Share Try the world leading quantitative analysis platform today Sign Up QuantConnect™ 2022. All Rights Reserved TECHNOLOGY Algorithm Lab Documentation Community Tutorials Data Library Learning Articles System Status COMPANY About Affiliates Our Blog Contact Pricing Integration Partners Terms & Conditions Privacy Policy

NumPy and Basic Pandas | Introduction To Financial Python on QuantConnect Source: NumPy and Basic Pandas | Introduction To Financial Python on QuantConnect Pricing Data Community Algorithm Lab Documentation Sign In learning center articles / Introduction To Financial Python NumPy and Basic Pandas 4/14 Author Jing Wu 2018-06-04 Introduction Now that we have introduced the fundamentals of Python, it's time to learn about NumPy and Pandas. NumPy NumPy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. It also has strong integration with Pandas, which is another powerful tool for manipulating financial data. Python packages like NumPy and Pandas contain classes and methods which we can use by importing the package: import numpy as np Basic NumPy Arrays A NumPy array is a grid of values… Ứng dụng: nối nghiên cứu với programming, USD, lãi suất và risk regime — đưa vào journal và playbook. DOI/OA chỉ là rail tham chiếu; nội dung chính là summary, takeaways và ứng dụng thị trường.