<jupyter_start><jupyter_text><jupyter_code>import seaborn as sns
import matplotlib.pyplot as plt
sns.distplot([0, 1, 2, 3, 4, 5], hist=False)

plt.show()
sns.set_theme(style="darkgrid")
df = sns.load_dataset("penguins")
sns.displot(
    df, x="flipper_length_mm", col="species", row="sex",
    binwidth=3, height=3, facet_kws=dict(margin_titles=True),
)
sns.set_theme(style="ticks")

df = sns.load_dataset("anscombe")


sns.lmplot(x="x", y="y", col="dataset", hue="dataset", data=df,
           col_wrap=2, ci=None, palette="muted", height=4,
           scatter_kws={"s": 50, "alpha": 1})
sns.set_theme(style="ticks")

# Load the penguins dataset
penguins = sns.load_dataset("penguins")

# Show the joint distribution using kernel density estimation
g = sns.jointplot(
    data=penguins,
    x="bill_length_mm", y="bill_depth_mm", hue="species",
    kind="kde",
)<jupyter_output><empty_output><jupyter_text>2.dataset car crashes<jupyter_code>import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df=sns.load_dataset("car_crashes")

sns.scatterplot(data = df, x = "speeding", y = "alcohol")

plt.show()
df=sns.load_dataset("car_crashes")
grid = sns.FacetGrid(df, col = "not_distracted", hue = "ins_losses", col_wrap=5)
grid.map(sns.scatterplot, "speeding", "alcohol")

grid.add_legend()

plt.show()


import numpy as np
import matplotlib.colors as col
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
#Data for a three dimensional line
z = np.linspace(0, 15, 1000)
x = np.sin(z)
y = np.cos(z)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot3D(x, y, z, 'grey')
#Data for three dimensional scattered points
z = 15 * np.random.random(100)
x = np.sin(z) + 0.1 * np.random.randn(100)
y = np.cos(z) + 0.1 * np.random.randn(100)
ax.scatter3D(x, y, z, c=z, cmap='Greens')
plt.show()
def f(x, y):
    return np.sin(np.sqrt(x ** 2 + y ** 2))
x = np.linspace(-6, 6, 30)
y = np.linspace(-6, 6, 30)
x, y = np.meshgrid(x, y)
z = f(x, y)
fig = plt.figure()
ax = plt.axes(projection='3d')
ax.contour3D(x,y,z,50, cmap='binary')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()<jupyter_output>/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:136: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
  return array(a, dtype, copy=False, order=order, subok=True)<jupyter_text>PROJE DENEMESİ:<jupyter_code>import pandas as pd
veri=pd.read_csv("https://raw.githubusercontent.com/robinske/password-data/master/passwords/part-00000-abca9f4b-5795-47ee-8382-f523480a532f.csv")
veri
veri.password
b=input("enter password:")
for b in veri.password() :
  print("hg")<jupyter_output><empty_output><jupyter_text>derste:<jupyter_code>data= {"name":"zafer","password":"<PASSWORD>"}
data2= {"name":"emre","password":"<PASSWORD>"}
x= input("name:")
y= input("password:")

if [x=="data== name "] and [y=="data==password"]:
  print("hg")
elif [x=="data2== name"] and [y=="data2==password"]:
  print("hg")
elif[x=="data== name"] and [y=="data!= password"]:
  print("tekrar dene!")
elif[x=="data2== name"] and [y=="data2!=password"]:
  print("tekrar dene!")<jupyter_output><empty_output>
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