<jupyter_start><jupyter_text>Pandas<jupyter_code>import pandas as pd
df = pd.read_csv("/arcgis/samplesdata/hurricanes_merged/hurr_dask_000.csv")
type(df)
df
df["latitude_merged"]
df.query('ISO_time != "1842-10-26 00:00:00"')<jupyter_output><empty_output><jupyter_text>MatplotlibSome samples taken from https://matplotlib.org/examples/pylab_examples/<jupyter_code>import matplotlib.pyplot as plt
import numpy as np

t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2*np.pi*t)
plt.plot(t, s)

plt.xlabel('time (s)')
plt.ylabel('voltage (mV)')
plt.grid(True)
plt.savefig("test.png")
plt.show()
import numpy as np
import matplotlib.pyplot as plt


n_groups = 5

means_men = (20, 35, 30, 35, 27)
std_men = (2, 3, 4, 1, 2)

means_women = (25, 32, 34, 20, 25)
std_women = (3, 5, 2, 3, 3)

fig, ax = plt.subplots()

index = np.arange(n_groups)
bar_width = 0.35

opacity = 0.4
error_config = {'ecolor': '0.3'}

rects1 = plt.bar(index, means_men, bar_width,
                 alpha=opacity,
                 color='b',
                 yerr=std_men,
                 error_kw=error_config,
                 label='Men')

rects2 = plt.bar(index + bar_width, means_women, bar_width,
                 alpha=opacity,
                 color='r',
                 yerr=std_women,
                 error_kw=error_config,
                 label='Women')

plt.xlabel('Group')
plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(index + bar_width / 2, ('A', 'B', 'C', 'D', 'E'))
plt.legend()

plt.tight_layout()
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as colors

fig, ax = plt.subplots()
Ntotal = 1000
N, bins, patches = ax.hist(np.random.rand(Ntotal), 20)

# I'll color code by height, but you could use any scalar


# we need to normalize the data to 0..1 for the full
# range of the colormap
fracs = N.astype(float)/N.max()
norm = colors.Normalize(fracs.min(), fracs.max())

for thisfrac, thispatch in zip(fracs, patches):
    color = cm.viridis(norm(thisfrac))
    thispatch.set_facecolor(color)


plt.show()<jupyter_output><empty_output><jupyter_text>SeabornTaken from https://seaborn.pydata.org/examples/<jupyter_code>import numpy as np
import pandas as pd
import seaborn as sns

sns.set()

# Generate an example radial datast
r = np.linspace(0, 10, num=100)
df = pd.DataFrame({'r': r, 'slow': r, 'medium': 2 * r, 'fast': 4 * r})

# Convert the dataframe to long-form or "tidy" format
df = pd.melt(df, id_vars=['r'], var_name='speed', value_name='theta')

# Set up a grid of axes with a polar projection
g = sns.FacetGrid(df, col="speed", hue="speed",
                  subplot_kws=dict(projection='polar'), height=4.5,
                  sharex=False, sharey=False, despine=False)

# Draw a scatterplot onto each axes in the grid
g.map(sns.scatterplot, "theta", "r")

import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")

# Load the example iris dataset
diamonds = sns.load_dataset("diamonds")

# Draw a scatter plot while assigning point colors and sizes to different
# variables in the dataset
f, ax = plt.subplots(figsize=(6.5, 6.5))
sns.despine(f, left=True, bottom=True)
clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="ch:r=-.2,d=.3_r",
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax)
import numpy as np
import pandas as pd
import seaborn as sns
sns.set(style="white")

# Generate a random correlated bivariate dataset
rs = np.random.RandomState(5)
mean = [0, 0]
cov = [(1, .5), (.5, 1)]
x1, x2 = rs.multivariate_normal(mean, cov, 500).T
x1 = pd.Series(x1, name="$X_1$")
x2 = pd.Series(x2, name="$X_2$")

# Show the joint distribution using kernel density estimation
g = sns.jointplot(x1, x2, kind="kde", height=7, space=0)
import seaborn as sns
sns.set(style="ticks", palette="pastel")

# Load the example tips dataset
tips = sns.load_dataset("tips")

# Draw a nested boxplot to show bills by day and time
sns.boxplot(x="day", y="total_bill",
            hue="smoker", palette=["m", "g"],
            data=tips)
sns.despine(offset=10, trim=True)<jupyter_output><empty_output>
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