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import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
Lets define a domain from -5 to 5, of 100 points, and plot some XY curves that show some functions.
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domain = np.linspace(-5.0,5.0,100)
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y = np.power(domain, 2)
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%matplotlib inline
# "magic" command telling Jupyter NB to embed plots
# always label and title your plot, at minimum
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Parabolic Curve")
p = plt.plot(domain, y)
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x3 = np.power(domain, 3)
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plt.xlabel("X")
plt.ylabel("Y")
plt.title("X to the 3rd Power")
p = plt.plot(domain, x3)
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def poly(x):
return (x - 3) * (x + 5) * (x - 1) * x
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Poly = np.vectorize(poly)
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y = Poly(domain)
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plt.xlabel("X")
plt.ylabel("Y")
plt.title("4th Degree Polynomial")
plt.grid()
p = plt.plot(domain, y)
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y0 = np.sin(domain)
y1 = np.cos(domain)
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plt.xlabel("X")
plt.ylabel("Y")
plt.title("Sine & Cosine")
plt.grid()
plt.plot(domain, y0, color = "orange", label="Sine")
plt.plot(domain, y1, color = "green", label="Cosine")
p = plt.legend()
Now that we've plotted some data, lets organize the data into a data table, or "data frame" to be more precise. Pandas is all about the DataFrame object.
Our domain is a 1-D ndarray lets remember. As such, we may turn it into a Series, which is the one dimensional equivalent in pandas. DataFrames are 2-dimensional and above.
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domain.shape
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col0 = pd.Series(domain)
col1 = pd.Series(np.power(domain,2))
col2 = pd.Series(x3)
col3 = Poly(domain)
datadict = {"Input":col0, "Parabola":col1, "3rd Power":col2, "Polynomial":col3}
df = pd.DataFrame(datadict, columns = ["Input", "Parabola", "3rd Power", "Polynomial"])
Without the columns argument, there's no guarantee that datadict will gives us the left-to-right column order we desire.
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df.head()
Here we're starting to introduce how data may be selected by numeric indexes, yes, but also by labels.
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df.loc[:,"3rd Power"].head()
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df.loc[:10,["Input", "3rd Power"]] # rows 0-10 inclusive, two columns