# Accomplish the following tasks by whatever means necessary based on the material we've covered in class. Save the notebook in this format: `<lastname>_DoNow_2-2.ipynb` where `<lastname>` is your last (family) name and turn it in via Slack.

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In [1]:

# the magic command to plot inline with the notebook
# https://ipython.org/ipython-doc/dev/interactive/tutorial.html#magic-functions
%matplotlib inline

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### 1. Import the pandas package and use the common alias

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In [2]:

import pandas as pd

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### 2. Read the file "heights_weights.xlsx" in the `data` folder into a pandas dataframe

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In [3]:

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### 3. Plot a histogram for both height and weight. Describe the data distribution in comments.

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In [4]:

df.hist()

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Out[4]:

array([[<matplotlib.axes.AxesSubplot object at 0x107359d10>,
<matplotlib.axes.AxesSubplot object at 0x1074131d0>]], dtype=object)

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### 4. Calculate the mean height and mean weight for the dataframe.

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In [5]:

df.mean()

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Out[5]:

height     62.336842
weight    100.026316
dtype: float64

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### 5. Calculate the other significant descriptive statistics on the two data points

• Standard deviation
• Range
• Interquartile range
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In [6]:

df.describe()

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Out[6]:

height
weight

count
19.000000
19.000000

mean
62.336842
100.026316

std
5.127075
22.773933

min
51.300000
50.500000

25%
58.250000
84.250000

50%
62.800000
99.500000

75%
65.900000
112.250000

max
72.000000
150.000000

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In [7]:

df['height'].max() - df['height'].min()

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Out[7]:

20.700000000000003

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In [9]:

df['height'].quantile(q=0.75) - df['height'].quantile(q=0.25)

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Out[9]:

7.6500000000000057

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### 6. Calculate the coefficient of correlation for these variables. Do they appear correlated? (put your answer in comments)

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In [10]:

df.corr()

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Out[10]:

height
weight

height
1.000000
0.877785

weight
0.877785
1.000000

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### Extra Credit: Create a scatter plot of height and weight

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