import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
z = np.arange(10)
a = np.arange(10)

# plot y over x and z over a in two different subplots
# Set "Y and Z" as a main title above the two subplots
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and invert the x axis
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.random.rand(10)
z = np.random.rand(10)
a = np.arange(10)

# Make two subplots
# Plot y over x in the first subplot and plot z over a in the second subplot
# Label each line chart and put them into a single legend on the first subplot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and label the x axis as "X"
# Make the line of the x axis red
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
plt.plot(y, x)
plt.xticks(range(0, 10, 2))

# Add extra ticks [2.1, 3, 7.6] to existing xticks
# SOLUTION START


import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

x = np.random.random(10)
y = np.random.random(10)
z = np.random.random(10)

# Make a 3D scatter plot of x,y,z
# change the view of the plot to have 100 azimuth and 50 elevation
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.random.rand(100) * 10

# Make a histogram of x
# Make the histogram range from 0 to 10
# Make bar width 2 for each bar in the histogram and have 5 bars in total
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# plot y over x with label "y"
# make the legend fontsize 8
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x in a line chart but use transparent marker with non-transparent edge
# SOLUTION START


import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.sin(x)

# draw a line plot of x vs y using seaborn and pandas
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.arange(10)
y = np.random.randn(10)

# line plot x and y with a thin diamond marker
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x with figsize (5, 5) and dpi 300
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# plot y over x
# use font size 20 for title, font size 18 for xlabel and font size 16 for ylabel
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np, pandas as pd
import seaborn as sns

tips = sns.load_dataset("tips")

# Make a seaborn joint regression plot (kind='reg') of 'total_bill' and 'tip' in the tips dataframe
# do not use scatterplot for the joint plot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.random.rand(10)
y = np.random.rand(10)

# Plot a grouped histograms of x and y on a single chart with matplotlib
# Use grouped histograms so that the histograms don't overlap with each other
# SOLUTION START


import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc

rc("mathtext", default="regular")

time = np.arange(10)
temp = np.random.random(10) * 30
Swdown = np.random.random(10) * 100 - 10
Rn = np.random.random(10) * 100 - 10

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(time, Swdown, "-", label="Swdown")
ax.plot(time, Rn, "-", label="Rn")
ax2 = ax.twinx()
ax2.plot(time, temp, "-r", label="temp")
ax.legend(loc=0)
ax.grid()
ax.set_xlabel("Time (h)")
ax.set_ylabel(r"Radiation ($MJ\,m^{-2}\,d^{-1}$)")
ax2.set_ylabel(r"Temperature ($^\circ$C)")
ax2.set_ylim(0, 35)
ax.set_ylim(-20, 100)
plt.show()
plt.clf()

# copy the code of the above plot and edit it to have legend for all three cruves in the two subplots
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

x = np.arange(10)
y = np.arange(10)

f = plt.figure()
ax = f.add_subplot(111)

# plot y over x, show tick labels (from 1 to 10)
# use the `ax` object to set the tick labels
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Make a scatter plot with x and y and remove the edge of the marker
# Use vertical line hatch for the marker
# SOLUTION START


import matplotlib.pyplot as plt

labels = ["Walking", "Talking", "Sleeping", "Working"]
sizes = [23, 45, 12, 20]
colors = ["red", "blue", "green", "yellow"]

# Make a pie chart with data in `sizes` and use `labels` as the pie labels and `colors` as the pie color.
# Bold the pie labels
# SOLUTION START


import seaborn as sns
import matplotlib.pylab as plt
import pandas
import numpy as np

df = pandas.DataFrame(
    {
        "a": np.arange(1, 31),
        "b": ["A",] * 10 + ["B",] * 10 + ["C",] * 10,
        "c": np.random.rand(30),
    }
)

# Use seaborn FaceGrid for rows in "b" and plot seaborn pointplots of "c" over "a"
# In each subplot, show xticks of intervals of 1 but show xtick labels with intervals of 2
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10, 20)
z = np.arange(10)

import matplotlib.pyplot as plt

plt.plot(x, y)
plt.plot(x, z)

# Give names to the lines in the above plot 'Y' and 'Z' and show them in a legend
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("penguins")[
    ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
]

# Make 2 subplots.
# In the first subplot, plot a seaborn regression plot of "bill_depth_mm" over "bill_length_mm"
# In the second subplot, plot a seaborn regression plot of "flipper_length_mm" over "bill_length_mm"
# Do not share y axix for the subplots
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.random.randn(10)
y = x
plt.scatter(x, y)

# put x ticks at 0 and 1.5 only
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

column_labels = list("ABCD")
row_labels = list("WXYZ")
data = np.random.rand(4, 4)
fig, ax = plt.subplots()
heatmap = ax.pcolor(data, cmap=plt.cm.Blues)

# Move the x-axis of this heatmap to the top of the plot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and show blue dashed grid lines
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

xvec = np.linspace(-5.0, 5.0, 100)
x, y = np.meshgrid(xvec, xvec)
z = -np.hypot(x, y)
plt.contourf(x, y, z)

# draw x=0 and y=0 axis in my contour plot with white color
# SOLUTION START


import numpy as np
import matplotlib.pyplot as plt

lines = [[(0, 1), (1, 1)], [(2, 3), (3, 3)], [(1, 2), (1, 3)]]
c = np.array([(1, 0, 0, 1), (0, 1, 0, 1), (0, 0, 1, 1)])

# Plot line segments according to the positions specified in lines
# Use the colors specified in c to color each line segment
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import numpy as np
import matplotlib.pyplot as plt

x = np.random.rand(10)
y = np.random.rand(10)
z = np.random.rand(10)

# plot x, then y then z, but so that x covers y and y covers z
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
z = np.arange(10)
a = np.arange(10)

# Plot y over x and z over a in two side-by-side subplots
# Make "Y" the title of the first subplot and "Z" the title of the second subplot
# Raise the title of the second subplot to be higher than the first one
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.arange(10)
y = np.random.randn(10)
plt.scatter(x, y)

# show xticks and vertical grid at x positions 3 and 4
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

a = [2.56422, 3.77284, 3.52623]
b = [0.15, 0.3, 0.45]
c = [58, 651, 393]

# make scatter plot of a over b and annotate each data point with correspond numbers in c
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# plot y over x with tick font size 10 and make the x tick labels vertical
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("exercise")

# Make catplots of scatter plots by using "time" as x, "pulse" as y, "kind" as hue, and "diet" as col
# Do not show any ylabel on either subplot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and label y axis "Y"
# Show y axis ticks on the left and y axis label on the right
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# plot y over x
# do not show xticks for the plot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Make a scatter plot with x and y
# Use vertical line hatch for the marker and make the hatch dense
# SOLUTION START


import matplotlib.pyplot as plt

# draw a circle centered at (0.5, 0.5) with radius 0.2
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("planets")
g = sns.boxplot(x="method", y="orbital_period", data=df)

# rotate the x axis labels by 90 degrees
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0.1, 2 * np.pi, 41)
y = np.exp(np.sin(x))

# make a stem plot of y over x and set the orientation to be horizontal
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x in a line chart. Show x axis tick labels but hide the x axis ticks
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data = {
    "reports": [4, 24, 31, 2, 3],
    "coverage": [35050800, 54899767, 57890789, 62890798, 70897871],
}
df = pd.DataFrame(data)
sns.factorplot(y="coverage", x="reports", kind="bar", data=df, label="Total")

# do not use scientific notation in the y axis ticks labels
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.random.rand(10)
y = np.random.rand(10)
bins = np.linspace(-1, 1, 100)

# Plot two histograms of x and y on a single chart with matplotlib
# Set the transparency of the histograms to be 0.5
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x in a line plot
# Show marker on the line plot. Make the marker have a 0.5 transparency but keep the lines solid.
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.random.randn(10)
y = np.random.randn(10)
sns.distplot(x, label="a", color="0.25")
sns.distplot(y, label="b", color="0.25")

# add legends
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# make a two columns and one row subplots. Plot y over x in each subplot.
# Give the plot a global title "Figure"
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x
# Show legend and use the greek letter lambda as the legend label
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# draw a full line from (0,0) to (1,2)
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot a scatter plot with values in x and y
# Plot the data points to have red inside and have black border
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.linspace(0, 2 * np.pi, 10)
y = np.cos(x)

# set xlabel as "X"
# put the x label at the right end of the x axis
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
plt.plot(x, y, marker="*", label="Line")

# Show a legend of this plot and show two markers on the line
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and use the greek letter phi for title. Bold the title and make sure phi is bold.
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# plot y over x on a 2 by 2 subplots with a figure size of (15, 15)
# repeat the plot in each subplot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

y = 2 * np.random.rand(10)
x = np.arange(10)
plt.plot(x, y)
myTitle = "Some really really long long long title I really really need - and just can't - just can't - make it any - simply any - shorter - at all."

# fit a very long title myTitle into multiple lines
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.arange(10)
y = 2 * np.random.rand(10)

# draw a regular matplotlib style plot using seaborn
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.arange(10)

# draw a line (with random y) for each different line style
# SOLUTION START


import numpy as np
import matplotlib.pyplot as plt

H = np.random.randn(10, 10)

# color plot of the 2d array H
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.random.randn(10)
y = np.random.randn(10)

# in plt.plot(x, y), use a plus marker and give it a thickness of 7
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.random.rand(10)
y = np.random.rand(10)
plt.scatter(x, y)

# how to turn on minor ticks on x axis only
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x in a line chart and label the line "y over x"
# Show legend of the plot and give the legend box a title
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Make a scatter plot with x and y
# Use star hatch for the marker
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
plt.plot(x, y, label="Line")
plt.plot(y, x, label="Flipped")

# Show a two columns legend of this plot
# SOLUTION START


import matplotlib.pyplot as plt

# Make a solid vertical line at x=3 and label it "cutoff". Show legend of this plot.
# SOLUTION START


import matplotlib.pyplot as plt

# draw vertical lines at [0.22058956, 0.33088437, 2.20589566]
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.linspace(0, 2 * np.pi, 400)
y1 = np.sin(x)
y2 = np.cos(x)

# plot x vs y1 and x vs y2 in two subplots, sharing the x axis
# SOLUTION START


from numpy import *
import math
import matplotlib
import matplotlib.pyplot as plt

t = linspace(0, 2 * math.pi, 400)
a = sin(t)
b = cos(t)
c = a + b

# Plot a, b, c in the same figure
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

x = np.arange(10)
y = np.sin(x)
df = pd.DataFrame({"x": x, "y": y})
sns.lineplot(x="x", y="y", data=df)

# remove x tick labels
# SOLUTION START


import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.DataFrame(
    np.random.randn(50, 4),
    index=pd.date_range("1/1/2000", periods=50),
    columns=list("ABCD"),
)
df = df.cumsum()

# make four line plots of data in the data frame
# show the data points  on the line plot
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.random.randn(10)
y = np.random.randn(10)

# in a scatter plot of x, y, make the points have black borders and blue face
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("exercise")

# Make catplots of scatter plots by using "time" as x, "pulse" as y, "kind" as hue, and "diet" as col
# Change the xlabels to "Exercise Time" and "Exercise Time"
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("penguins")[["bill_length_mm", "species", "sex"]]

# Use seaborn factorpot to plot multiple barplots of "bill_length_mm" over "sex" and separate into different subplot columns by "species"
# Do not share y axis across subplots
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

points = [(3, 5), (5, 10), (10, 150)]

# plot a line plot for points in points.
# Make the y-axis log scale
# SOLUTION START


import matplotlib.pyplot as plt

l = ["a", "b", "c"]
data = [225, 90, 50]

# Make a donut plot of using `data` and use `l` for the pie labels
# Set the wedge width to be 0.4
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("whitegrid")
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips)

# set the y axis limit to be 0 to 40
# SOLUTION START


import matplotlib.pyplot as plt
import numpy

xlabels = list("ABCD")
ylabels = list("CDEF")
rand_mat = numpy.random.rand(4, 4)

# Plot of heatmap with data in rand_mat and use xlabels for x-axis labels and ylabels as the y-axis labels
# Make the x-axis tick labels appear on top of the heatmap and invert the order or the y-axis labels (C to F from top to bottom)
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

d = np.random.random((10, 10))

# Use matshow to plot d and make the figure size (8, 8)
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x and label the x axis as "X"
# Make both the x axis ticks and the axis label red
# SOLUTION START


import numpy as np
import matplotlib.pyplot as plt

H = np.random.randn(10, 10)

# show the 2d array H in black and white
# SOLUTION START


import matplotlib
import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame(
    {
        "celltype": ["foo", "bar", "qux", "woz"],
        "s1": [5, 9, 1, 7],
        "s2": [12, 90, 13, 87],
    }
)

# For data in df, make a bar plot of s1 and s1 and use celltype as the xlabel
# Make the x-axis tick labels rotate 45 degrees
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset("penguins")[
    ["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"]
]
sns.distplot(df["bill_length_mm"], color="blue")

# Plot a vertical line at 55 with green color
# SOLUTION START


import matplotlib.pyplot as plt
import numpy as np

# Specify the values of blue bars (height)
blue_bar = (23, 25, 17)
# Specify the values of orange bars (height)
orange_bar = (19, 18, 14)

# Plot the blue bar and the orange bar side-by-side in the same bar plot.
# Make  sure the bars don't overlap with each other.
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)
fig, ax = plt.subplots(1, 1)
plt.xlim(1, 10)
plt.xticks(range(1, 10))
ax.plot(y, x)

# change the second x axis tick label to "second" but keep other labels in numerical
# SOLUTION START


import matplotlib.pyplot as plt

fig, axes = plt.subplots(ncols=2, nrows=2, figsize=(8, 6))
axes = axes.flatten()

for ax in axes:
    ax.set_ylabel(r"$\ln\left(\frac{x_a-x_b}{x_a-x_c}\right)$")
    ax.set_xlabel(r"$\ln\left(\frac{x_a-x_d}{x_a-x_e}\right)$")

plt.show()
plt.clf()

# Copy the previous plot but adjust the subplot padding to have enough space to display axis labels
# SOLUTION START


import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

x = np.arange(10)
y = np.arange(10)

# Plot y over x in a line chart. Show x axis ticks on both top and bottom of the figure.
# SOLUTION START