By Christopher van Hoecke, Maxwell Margenot
https://www.quantopian.com/lectures/introduction-to-pandas
This lecture corresponds to the Introduction to Pandas lecture, which is part of the Quantopian lecture series. This homework expects you to rely heavily on the code presented in the corresponding lecture. Please copy and paste regularly from that lecture when starting to work on the problems, as trying to do them from scratch will likely be too difficult.
Part of the Quantopian Lecture Series:
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# Useful Functions
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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l = np.random.randint(1,100, size=1000)
s = pd.Series(l)
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symbol = "CMG"
start = "2012-01-01"
end = "2016-01-01"
prices = get_pricing(symbol, start_date=start, end_date=end, fields="price")
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print "Summary Statistics"
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data = get_pricing('GE', fields='open_price', start_date='2016-01-01', end_date='2017-01-01')
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# Rolling mean
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# Rolling Standard Deviation
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l = {'fifth','fourth', 'third', 'second', 'first'}
dict_data = {'a' : [1, 2, 3, 4, 5], 'b' : ['L', 'K', 'J', 'M', 'Z'],'c' : np.random.normal(0, 1, 5)}
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s1 = pd.Series([2, 3, 5, 7, 11, 13], name='prime')
s2 = pd.Series([1, 4, 6, 8, 9, 10], name='other')
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symbol = ["XOM", "BP", "COP", "TOT"]
start = "2012-01-01"
end = "2016-01-01"
prices = get_pricing(symbol, start_date=start, end_date=end, fields="price")
if isinstance(symbol, list):
prices.columns = map(lambda x: x.symbol, prices.columns)
else:
prices.name = symbol
# Check Type of Data for these two.
prices.XOM.head()
prices.loc[:, 'XOM'].head()
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prices) to only print values where:nan values ((BP > 30 and XOM < 100) or TOT is non-NaN).
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# Filter the data for prices to only print out values where
# BP > 30
# XOM < 100
# BP > 30 AND XOM < 100
# The union of (BP > 30 AND XOM < 100) with TOT being non-nan
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# Add a column for TSLA and drop the column for XOM
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# Concatenate these dataframes
df_1 = get_pricing(['SPY', 'VXX'], start_date=start, end_date=end, fields='price')
df_2 = get_pricing(['MSFT', 'AAPL', 'GOOG'], start_date=start, end_date=end, fields='price')
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# Fill GOOG missing data with 0
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prices DataFrame from above.
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# Print a summary of the 'prices' times series.
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# Print the natural log returns of the first 10 values
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# Print the Muliplicative returns
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# Normlalize the returns and plot
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# Rolling mean
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# Rolling standard deviation
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# Plotting
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Congratulations on completing the Introduction to pandas exercises!
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