quiz 3 data visualization

Question 1

2 types of data visualization

Answer 1

declaritive purpose and exploritory purpose to make better decisions

Question 2

3 types of data

Answer 2

quantitative, categorical, and ordinal

Question 3

quantitative data

Answer 3

descrete and continuous

Question 4

categorical

Answer 4

nominal(order doesnt matter) and only subset of values
M or F
Hair is blonde, brunette, black etc.
who cares about order

Question 5

ordinal

Answer 5

subset of values but order does matter
such as low,med, high
income low,med,high
education high school, college

Question 6

Find the Median of: 9, 3, 44, 17, 15

Answer 6

3,9,15,17,44 median is 17. line up ascending pick middle

Question 7

8, 3, 44, 17, 12, 6

Answer 7

3,6,8,12,17,44. since even amount 8+12/2=10

Question 8

explain why a plot could be helpful

Answer 8

as part of the exploratory analysis to identify outliers. or s part of the end goal.

Question 9

create plot with a range of 0-9

Answer 9

data=np.arange(9)

x=plt.plot(data)

Question 10

create a plot with -1,3,5,7

Answer 10

x=plt.plot([-1,3,5,7])

Question 11

create an empty figure object

Answer 11

x=plt.figure(). You can’t make it appear without subplots though.
ax1 = fig.add_subplot(2, 2, 1)

Question 12

create a figure object with a random array of 10

Answer 12

data=np.random.randn(10)

Question 13

create figure object 1x1 with plot of 1.5,3,5,-2,1.6

Answer 13

fig=plt.figure() #Create a figure object
ax=fig.add_subplot(1,1,1) #create a AxesSubplot object
ax.plot([1.5,3.5,-2,1.6])

Question 14

plot a series array

Answer 14

ser=pd.Series(np.random.randn(10).cumsum(),index=np.arange(0,100,10)
ser.plot
#The Series object’s index is passed to matplotlib for plotting on the x-axis, though you
can disable this by passing use_index=False.

Question 15

plot a dataframe

Answer 15

df=pd.DataFrame(np.random.randn(10,4).cumsum(0), columns=[‘A’,’B’,’C’,’D’],index=np.arange(0,100,10))
df.plot()
this will make 4 random dataframes labels abcd

Question 16

create 3 series bar and horizonal bar plots

Answer 16

#fig, axes=plt.subplots(2,1)
fig, axes=plt.subplots(3, 1)
#data = pd.Series(np.random.randn(16),index=list('abcdefghijklmnop'))
data = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))
data1 = pd.Series(np.random.rand(16), index=list('abcdefghijklmnop'))
data.plot.bar(ax=axes[0],color='k',alpha=.7)
data.plot.barh(ax=axes[1],color='r',alpha=.7)
data.plot.barh(ax=axes[2],color='g',alpha=.7)

Question 17

create a dataframe with 6 of 4 columns in vertical bar chart.

Answer 17

df=pd.DataFrame(np.random.rand(6,4),index=[‘one’,’two’,’three’,’four’,’five’,’six’],columns=pd.Index[‘A’,’B’,’C’,’D’],,name=’Genus’))
df.plot.bar()

Question 18

create same dataframe but horizonal stacked

Answer 18

df=pd.DataFrame(np.random.rand(6,4),index=[‘one’,’two’,’three’,’four’,’five’,’six’],columns=pd.Index[‘A’,’B’,’C’,’D’],,name=’Genus’))
df.plot.barh(stacked=True, alpha=0.9)

Question 19

what are black lines on bar chart

Answer 19

95% confidence level

Question 20

lim,

xticks, and xticklabels

Answer 20

The pyplot interface, designed for interactive use, consists of methods like xlim,
xticks, and xticklabels. These control the plot range, tick locations, and tick labels,
respectively. They can be used in two ways:

Question 21

put the x axis to 0 to 10

Answer 21

lt.xlim([0, 10]), sets

the x-axis range to 0 to 10)

Question 22

1 figure cumulitive random 1-1000

Answer 22

In [37]: fig = plt.figure()
In [38]: ax = fig.add_subplot(1, 1, 1)
In [39]: ax.plot(np.random.randn(1000).cumsum())

Question 23

change x axis ticks and labels

Answer 23

To change the x-axis ticks, it’s easiest to use set_xticks and set_xticklabels. The
former instructs matplotlib where to place the ticks along the data range; by default
ticks = ax.set_xticks([0, 250, 500, 750, 1000])
labels = ax.set_xticklabels([‘one’, ‘two’, ‘three’, ‘four’, ‘five’],
….: rotation=30, fontsize=’small’)
set title:
ax.set_title(‘My first matplotlib plot’)

Question 24

how do you create a legend

Answer 24

first put labels for each subplot , then call the legend
f4.plot(np.random.randn(50).cumsum(),'k--',label='black')
f4.plot(np.random.randn(40).cumsum(),'b--',label='blue')
#f4.legend()
f4.legend(loc='best')

Question 25

how to save a plot figure

Answer 25

plt.savefig(‘figpath.svg’)