# Bar plot and proportion from data - 11:00 AM

edited February 1

(10 points)

Using your own random data from last week's forum question, let's use Colab to examine the `activity_level` variable. Specifically:

• Generate a bar chart for `activity_level` and
• Compute the proportion of folks whose activity level is `high`.

Note that creative burden is higher in this lab than in the last in that the Colab link above leads to a blank notebook. Nonetheless, you can find sample code that should help in our class presentation on Categorical Data.

• edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for `activity_level` like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high 39
moderate 33
none 28

I can now see the proportion is
$39/100 = 0.39$.

Finally, I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

My data looked like this:

``````import pandas as pd
``````

I isolated the activity level with this code:

``````value_counts = df['activity_level'].value_counts()
value_counts
#. Output: moderate     42
high            30
none           28
``````

So now I can see the proportion of high activity level to all participants is:

$30/100 = 0.3$

Here is my bar chart

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for 'activity level' like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high 37
moderate 27
none 36

I can now see that the proportion is
$37/100=0.37$

Finally, I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for 'activity_level' like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high 40
moderate 33
none 27

I can now see the proportion is

$40/100 = 0.40$.

Finally, I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • I got my data using this code

``````import pandas as pd
``````

Then I generated a bar chart for 'activity_level' with this code

``````value_counts = df['activity_level'].value_counts()
value_counts.plot.bar()
`````` I can now see the proportion is

$27/100 = 0.27$

• I got my data using this Code:

``````import pandas as pd
``````

I then generated a table for activity_level like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

Moderate 39
High 34
None 27

I can now see the proportion is:

$39/100$ = 0.39

finally I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for 'activity_level' using the code:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

moderate 44
high 32
none 24

I can now see the proportion is
$32/100 = 0.32$

Finally, I generated my bar chart using this code:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • I got my data using this code

``````import pandas as pd
``````

Then I generated a table for " activity _level" like so

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high 39
none 35
moderate 26

I can now see the proportion is
$39/100$ = 0.39 • I got my data using:

``````import pandas as pd
``````

Then I generated a table for 'activity_level' by doing this:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

moderate 42
high 30
none 28

Now, we can see the proportion is
$42/100 = 0.42$

And this is the bar chart:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • I got my data using this code:

``````import pandas as pd
``````

I then generated a table for 'activity_level' like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high 40
moderate 30
none 30

I can now see the proportion is
$40/100 = 0.40$.

I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
``````

Here is my bar chart: • edited February 1

I got my data using code:

``````import pandas as pd
``````

I then generated a table for 'activity level' like so:

``````value_counts = df['activity_level'].value_counts() value_counts
``````

none 37
high 32
moderate 31

I can now see the proportion is:

$32/100 = 0.32$

Final, I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for"activity_level":

`````` value_counts = df['activity_level'].value_counts()
value_counts
``````

moderate 36
high 32
none 32

I can now see the proportion is
$32/100=0.32$.

finally, I generated my bar chart like so:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

Then I generated a table for 'activity_level' with this code:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

None:36
High:35
Moderate:29

I can now see the proportion is
$35/100=0.35$

Finally I generated my bar chart like this:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using:

``````import pandas as pd
``````

then I generated a table for 'activity_level' like so:

``````high        37
moderate    36
none        27
Name: activity_level, dtype: int64
``````

I can now see the proportion is
$37/100 =0.37$

finally, I generated my bar chart like so:

``````  value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

i got my data using this code:

``````import pandas as pd
``````

i generated a table for my data with this:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

high: 39
moderate: 33
none: 28

the proportion is clearly:
39/100 = .39

here is my bar chart:

`````` value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code

``````import pandas as pd
``````

I isolated the activity level with this code

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

none 40
moderate 32
high 28
Name: activity_level, dtype: int64

I can now see that my proportion is
28/100= 0.28

I generated my bar chart using the code

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1

I got my data using this code:

``````import pandas as pd
``````

I then generated a table for activity_level like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
moderate    38
high        34
none        28
``````

I can now see the proportion is

$34/100= 0.34$

Finally, I generated my bar chart using this code:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • I got my data using code:

``````import pandas as pd
``````

I then generated a table for 'activity level' like so:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

none 43
moderate 29
High 28

I can Now see the proportion is

$43/100$

I then created a bar graph:

``````value_counts.plot.bar(figsize=(12,7), rot = 0);
`````` • edited February 1
``````import pandas as pd
``````

I found the values like this:
value_counts = df["activity_level"].value_counts()
value_counts
none-41
moderate-30
high-29

I now see the proportion is
29/100= 0.29

Finally I generated my bar chart like so:
value_counts.plot.bar(figsize=(12,7), rot = 0); • edited February 1

I got my data using this code:

``````import pandas as pd
``````

Then I generated a table for " activity _level" using the code:

``````value_counts = df['activity_level'].value_counts()
value_counts
``````

High 39
None 35
Moderate 26

I can now see the proportion

$39/100$ = .39

Finally I generated my bar graph by using the code:

``````value_counts.plot.bar(figsize=(12,7), rot = 0)
`````` 