Subversion Repositories Boeken.quickpython

# coding: utf-8

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get_ipython().system('pip install pandas matplotlib numpy')


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get_ipython().magic('matplotlib inline')
import pandas as pd
import numpy as np


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grid = [[1,2,3], [4,5,6], [7,8,9]]
print(grid)

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]


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import pandas as pd
df = pd.DataFrame(grid)
print(df)


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df = pd.DataFrame(grid, columns=["one", "two", "three"] )
print(df)


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print(df["two"])


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print([x[1] for x in grid])
[2, 5, 8]


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for x in df["two"]:
    print(x)


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edges = df[["one", "three"]]
print(edges)


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print(edges.add(2))


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df['two'].value_counts()


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mars = pd.read_json("mars_data_01.json")
print(mars)


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temp = pd.read_csv("temp_data_01.csv", header=0, names=range(18), usecols=range(4,18))


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print(temp)


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temp = pd.read_csv("temp_data_01.csv", na_values=['Missing'], header=0, names=range(18), usecols=range(4,18))
print(temp)


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temp[17][0]


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temp[17]=temp[17].str.strip("%")
temp[17][0]


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temp[17][0]


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temp[17] = pd.to_numeric(temp[17])
temp[17][0]


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temp[17] = temp[17].div(100)
temp[17]


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calls = pd.read_csv("sales_calls.csv")
print(calls)


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revenue = pd.read_csv("sales_revenue.csv")
print(revenue)


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calls_revenue = pd.merge(calls, revenue, on=['Territory', 'Month'])
print(calls_revenue)


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print(calls_revenue[calls_revenue.Territory==3])


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print(calls_revenue[calls_revenue.Amount/calls_revenue.Calls>500])


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calls_revenue['Call_Amount'] = calls_revenue.Amount/calls_revenue.Calls
print(calls_revenue)


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print(calls_revenue.Calls.sum())
print(calls_revenue.Calls.mean())
print(calls_revenue.Calls.median())
print(calls_revenue.Calls.max())
print(calls_revenue.Calls.min())


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print(calls_revenue[calls_revenue.Call_Amount >= calls_revenue.Call_Amount.median()])
print(calls_revenue.Call_Amount.median())


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print(calls_revenue[['Month', 'Calls', 'Amount']].groupby(['Month']).sum())


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print(calls_revenue[['Territory', 'Calls', 'Amount']].groupby(['Territory']).sum())


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calls_revenue[['Territory', 'Calls']].groupby(['Territory']).sum().plot.bar()


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calls_revenue[['Month', 'Call_Amount']].groupby(['Month']).mean().plot()


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