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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Data Visualisation
Step1:  Chart Attributes
Pyplot - Size, Title, Axes Labels, Axes Range
Step2:  Axes - Set Range
python
ax.set_ylim([0, 1])
ax.set_xlim([0, 1])
Count of Each Label in Column of DataFrame (Histogram)
Step3:  Percentage of Each Label in Column of DataFrame
Step4:  Count of (Continuous) Values in Column of DataFrame
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Python Code:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
Explanation: Data Visualisation
End of explanation
plt.figure(figsize = [15, 5])
plt.title('My plot')
plt.xlabel('X values')
plt.ylabel('Count of values')
plt.xlim([0, 10])
plt.ylim([0, 5])
Explanation: Chart Attributes
Pyplot - Size, Title, Axes Labels, Axes Range
End of explanation
data_dict = {
    'x': [0, 1, 1, 2, 1, 0, 0, 1, 2, 1, 1],
    'y': [1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 1]
}
df = pd.DataFrame(data=data_dict)
plt.title('My plot')
plt.xlabel('X values')
plt.ylabel('Count of values')
sns.countplot(df['x']);
Explanation: Axes - Set Range
python
ax.set_ylim([0, 1])
ax.set_xlim([0, 1])
Count of Each Label in Column of DataFrame (Histogram)
End of explanation
data_dict = {
    'x': [0, 1, 1, 2, 1, 0, 0, 1, 2, 1, 1],
    'y': [1, 1, 1, 1, 1, 0, 0, 0, 1, 2, 1]
}
df = pd.DataFrame(data=data_dict)
percentage_dist = df['x'].value_counts(normalize=True).sort_index()
ax = sns.barplot(x=percentage_dist.index, y=percentage_dist.values)
ax.set_ylim([0,1]);
Explanation: Percentage of Each Label in Column of DataFrame
End of explanation
mu, sigma = 0, 0.1 # mean and standard deviation
data_dict = {
    'x': np.random.normal(mu, sigma, 1000)
}
df = pd.DataFrame(data=data_dict)
plt.hist(df['x'], bins=np.arange(start=-0.4, stop=0.4, step=0.05));
Explanation: Count of (Continuous) Values in Column of DataFrame
End of explanation
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