import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

x = pd.Series([1,2,3,4,5])
x

x + 100

(x ** 2) + 100

x > 2

larger_than_2 = x > 2
larger_than_2

larger_than_2.any()

larger_than_2.all()

def f(x):
    if x % 2 == 0:
        return x * 2
    else:
        return x * 3

x.apply(f)

%%timeit

ds = pd.Series(range(10000))

for counter in range(len(ds)):
    ds[counter] = f(ds[counter])

%%timeit

ds = pd.Series(range(10000))

ds = ds.apply(f)

x.astype(np.float64)

y = x

y[0]

y[0] = 100

y

x

y = x.copy()

x[0]=1

x

y

x.describe(percentile_width=50)

data = [1,2,3,4,5,6,7,8,9]
df = pd.DataFrame(data, columns=["x"])

df

df["x"]

df["x"][0]

df["x_plus_2"] = df["x"] + 2
df

df["x_square"] = df["x"] ** 2
df["x_factorial"] = df["x"].apply(np.math.factorial)
df

df["is_even"] = df["x"] % 2
df

df["odd_even"] = df["is_even"].map({1:"odd", 0:"even"})
df

df = df.drop("is_even", 1)
df

df[["x", "odd_even"]]

pd.options.display.max_columns= 60
pd.options.display.max_rows= 6
pd.options.display.notebook_repr_html = False
df

df[df["odd_even"] == "odd"]

df[df.odd_even == "even"]

df[(df.odd_even == "even") | (df.x_square < 20)]

df[(df.odd_even == "even") & (df.x_square < 20)]

df[(df.odd_even == "even") & (df.x_square < 20)]["x_plus_2"][:1]

pd.scatter_matrix(df, diagonal="kde", figsize=(10,10));

df.describe()

url = "http://www.google.com/finance/historical?q=TADAWUL:TASI&output=csv"
stocks_data = pd.read_csv(url)

stocks_data

stocks_data["change_amount"] = stocks_data["Close"] - stocks_data["Open"]
stocks_data["change_percentage"] = stocks_data["change_amount"] / stocks_data["Close"]
stocks_data