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import pyplot from matplotlib as plt
plt.hist(x_axis_list, y_axis_list)
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import matplotlib.pyplot as plt
data = [1.7,1.8,2.0,2.2,2.2,2.3,2.4,2.5,2.5,2.5,2.6,2.6,2.8,
2.9,3.0,3.1,3.1,3.2,3.3,3.5,3.6,3.7,4.1,4.1,4.2,4.3]
#this histogram has a range from 1 to 4
#and 8 different bins
plt.hist(data, range=(1,4), bins=8)
plt.show()
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import matplotlib.pyplot as plt
data = [1.7,1.8,2.0,2.2,2.2,2.3,2.4,2.5,2.5,2.5,2.6,2.6,2.8,
2.9,3.0,3.1,3.1,3.2,3.3,3.5,3.6,3.7,4.1,4.1,4.2,4.3]
plt.hist(data)
plt.title('Histogram of Data')
plt.xlabel('data')
plt.ylabel('count')
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import matplotlib.pyplot as plt
x = [1, 1, 2, 3, 3, 5, 7, 8, 9, 10,
10, 11, 11, 13, 13, 15, 16, 17, 18, 18,
18, 19, 20, 21, 21, 23, 24, 24, 25, 25,
25, 25, 26, 26, 26, 27, 27, 27, 27, 27,
29, 30, 30, 31, 33, 34, 34, 34, 35, 36,
36, 37, 37, 38, 38, 39, 40, 41, 41, 42,
43, 44, 45, 45, 46, 47, 48, 48, 49, 50,
51, 52, 53, 54, 55, 55, 56, 57, 58, 60,
61, 63, 64, 65, 66, 68, 70, 71, 72, 74,
75, 77, 81, 83, 84, 87, 89, 90, 90, 91
]
plt.hist(x, bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 99])
plt.show()
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import matplotlib.pyplot as plt
fig, ax = plt.subplots
ax.hist(df["col1"], label="Col1", bins=[150, 160, 170, 180, 190, 200, 210], histtype="step")
ax.hist(df["col2"], label="Col2", bins=[150, 160, 170, 180, 190, 200, 210], histtype="step")
ax.set_xlabel("X axis label")
ax.set_ylabel("Y axis label")
ax.legend()
plt.show()
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# Method 1
plt.hist(df["col"])
# Method 2
df["col"].plot(kind = 'hist')
plt.show()
# Method 3 : Fine-tuning
count, bin_edges = np.histogram(df["col"])
df["col"].plot(kind = 'hist', xticks = bin_edges)
# Method 4
sns.histplot(df, x="col")
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import pyplot from matplotlib as plt
plt.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)
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import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colors
from matplotlib.ticker import PercentFormatter
# Creating dataset
np.random.seed(23685752)
N_points = 10000
n_bins = 20
# Creating distribution
x = np.random.randn(N_points)
y = .8 ** x + np.random.randn(10000) + 25
legend = ['distribution']
# Creating histogram
fig, axs = plt.subplots(1, 1,
figsize =(10, 7),
tight_layout = True)
# Remove axes splines
for s in ['top', 'bottom', 'left', 'right']:
axs.spines[s].set_visible(False)
# Remove x, y ticks
axs.xaxis.set_ticks_position('none')
axs.yaxis.set_ticks_position('none')
# Add padding between axes and labels
axs.xaxis.set_tick_params(pad = 5)
axs.yaxis.set_tick_params(pad = 10)
# Add x, y gridlines
axs.grid(b = True, color ='grey',
linestyle ='-.', linewidth = 0.5,
alpha = 0.6)
# Add Text watermark
fig.text(0.9, 0.15, 'Jeeteshgavande30',
fontsize = 12,
color ='red',
ha ='right',
va ='bottom',
alpha = 0.7)
# Creating histogram
N, bins, patches = axs.hist(x, bins = n_bins)
# Setting color
fracs = ((N**(1 / 5)) / N.max())
norm = colors.Normalize(fracs.min(), fracs.max())
for thisfrac, thispatch in zip(fracs, patches):
color = plt.cm.viridis(norm(thisfrac))
thispatch.set_facecolor(color)
# Adding extra features
plt.xlabel("X-axis")
plt.ylabel("y-axis")
plt.legend(legend)
plt.title('Customized histogram')
# Show plot
plt.show()
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# 2. Histogram - Age distribution of passengers
ages = titanic_df.age
plt.hist(x=ages, bins=20, edgecolor='black')
plt.title('Age Distribution of Passengers')
plt.ylabel('Frequency')
plt.xlabel('Age')
plt.show()
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>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), density=True)
(array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))