Udemy Visualization

Question 1

When using jupyter notebook, how can you see plots that you create with matplotlib?

Answer 1

%matplotlib inline

Question 2

Object-oriented way to make an x, y line plot using matplotlib

Answer 2

fig = plt.figure() #essentially creates an empty canvas

axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])

axes.plot(x, y)-===============================

Question 3

Object-oriented way to create subplots using matplotlib

Answer 3

fig, axes = plt.subplots(nrows=1, ncols=2)

axes[0].plot(x,y)

axes[0].set_title(‘First Plot’)

axes[1].plot(y, x)

axes[1].set_title(‘Second Plot’)

Question 4

What usually takes cares of overlapping plots?

Answer 4

plt.tight_layout()

Question 5

How can you control the figure size?

Answer 5

fig = plt.figure(figsize=(width, height))

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(width, height))

Question 6

How do you save a figure?

Answer 6

fig.savefig(‘my_picture.png’, dpi=200)

Question 7

Object-oriented way to add y and x label and title

Answer 7

ax. set_ylabel(‘Y’)
ax. set_xlabel(‘X’)
ax. set_title(‘Title’)

Question 8

How to add a legend

Answer 8

Add label to each plot and follow with ax.legend()

fig = plt.figure()

ax = fig.add_axes([0,0,1,1])

ax. plot(x, x**2, label =‘X Squared’)
ax. plot(x, x**3, label =‘X Cubed’)

ax.legend()

Question 9

How do you change position of the legend?

Answer 9

number 0-10 for different positions. Use 0 to make it find the “best” location

ax.legend(loc = number)

Question 10

How do you add color to a line plot?

Answer 10

color can be color name, e.g. ‘green’, or RGB Hex Code starting with

ax.plot(x, y, color = ‘color’)

Question 11

How do you modify the line width and transparency?

Answer 11

alpha defines transparency

ax.plot(x, y, linewidth = num, alpha = num)

or

ax.plot(x, y, lw = num, alpha = num)

Question 12

How do you modify the linestyle?

Answer 12

style looks like ‘–’ or ‘:’ . More styles available

ax.plot(x, y, linestyle = ‘style’)

or

ax.plot(x, y, ls = ‘style’)

Question 13

How do you add markers to each point on a line plot?

How do you modify the marker size?

Answer 13

Several marker styles available

ax.plot(x, y, marker = ‘marker’, markersize = num)

Question 14

How do you set the max num in the x and y axes?

Answer 14

ax. set_xlim([lower, upper])
ax. set_ylim([lower, upper])

Question 15

seaborn is built on top of

Answer 15

matplotlib

Question 16

How do you make a histogram with seaborn?

Answer 16

This will also plot a kde on top unless set kde = False

sns.distplot(df[col1], bins=num)

Question 17

Use seasborn to compare distributions of two variables

Answer 17

kind=scatter is default, can also use ‘reg’ for regression line, etc

sns.jointplot(x=’col1’, y=’col2’, data=df, kind=’scatter’)

Question 18

How do you plot pairwise relationships across entire dataframe in Seaborn?

Answer 18

will produce scatter plots for all pairs of numerical values

sns.pairplot(df)

sns.pairplot(df, hue=’col2’)

Question 19

Draw dash mark for every single point in col1 to show distribution using Seaborn

Answer 19

sns.rugplot(df[‘col1’])

Question 20

Draw KDE plot using Seaborn

Answer 20

sns.kdeplot(df[‘col1’])

Question 21

Create a barplot using Seaborn

Answer 21

x is categorical, y is numerical

sns.barplot(x=’col1’, y=’col2’, data=df)

Question 22

Make a barplot of the counts of a categorical variable using Seaborn

Answer 22

sns.countplot(x=’col1’, data=df)

Question 23

Make a boxplot using Seaborn

Answer 23

x is categorical, y is numerical

sns.boxplot(x=’col1’, y=’col2’, data=df, hue=’col3’)

Question 24

Create violinplot using Seaborn

Answer 24

x is categorical, y is numerical

sns.violinplot(x=’col1’, y=’col2’, data=df)

Question 25

Create a striplot using seaborn

Answer 25

x is categorical, y is numerical

sns.stripplot(x=’col1’, y=’col2’, data=df, jitter=True)

Question 26

Create a swarmplot using Seaborn

Answer 26

Combo of striplot and violin plot

sns.swarmplot(x=’col1’, y=’col2’, data=df)

Question 27

what does sns.factorplot do?

Answer 27

It allows you to create any kind of plot with its kind= option

Question 28

for a heatmap to work properly, data should be in what form?

Answer 28

Matrix form. Can either use df.corr() or pivot table method to turn df into matrix

Question 29

Heatmap and clustermap using seaborn

Answer 29

sns. heatmap(matrix)
sns. clustermap(matrix)

Question 30

Create a simple regression plot with Seaborn

Answer 30

sns.lmplot(x=’col1’, y=’col2’, data=df)

Question 31

how can you look up the palettes used in seaborn?

Answer 31

Google matplotlib colormap