import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(np.std(arr))
1.4142135623730951
#74.
np.sum()Sums array elements over a given axis.
import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(np.sum(arr))
10
#75.
np.min()Returns the minimum of an array or minimum along an axis.
import numpy as np
arr = np.array([5, 2, 8, 1])
print(np.min(arr))
1
#76.
np.max()Returns the maximum of an array or maximum along an axis.
import numpy as np
arr = np.array([5, 2, 8, 1])
print(np.max(arr))
8
#77.
np.sqrt()Returns the non-negative square-root of an array, element-wise.
import numpy as np
arr = np.array([4, 9, 16])
print(np.sqrt(arr))
[2. 3. 4.]
#78.
np.log()Calculates the natural logarithm, element-wise.
import numpy as np
arr = np.array([1, np.e, np.e**2])
print(np.log(arr))
[0. 1. 2.]
#79.
np.dot()Calculates the dot product of two arrays.
import numpy as np
a = np.array([1, 2])
b = np.array([3, 4])
print(np.dot(a, b))
11
#80.
np.where()Returns elements chosen from x or y depending on a condition.
import numpy as np
arr = np.array([10, 5, 20, 15])
print(np.where(arr > 12, 'High', 'Low'))
['Low' 'Low' 'High' 'High']
---
#DataAnalysis #Matplotlib #Seaborn #Visualization
Part 8: Matplotlib & Seaborn - Data Visualization
#81.
plt.plot()Plots y versus x as lines and/or markers.
import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
# In a real script, you would call plt.show()
print("Output: A figure window opens displaying a line plot.")
Output: A figure window opens displaying a line plot.
#82.
plt.scatter()A scatter plot of y vs. x with varying marker size and/or color.
import matplotlib.pyplot as plt
plt.scatter([1, 2, 3, 4], [1, 4, 9, 16])
print("Output: A figure window opens displaying a scatter plot.")
Output: A figure window opens displaying a scatter plot.
#83.
plt.hist()Computes and draws the histogram of x.
import matplotlib.pyplot as plt
import numpy as np
data = np.random.randn(1000)
plt.hist(data, bins=30)
print("Output: A figure window opens displaying a histogram.")
Output: A figure window opens displaying a histogram.
#84.
plt.bar()Makes a bar plot.
import matplotlib.pyplot as plt
plt.bar(['A', 'B', 'C'], [10, 15, 7])
print("Output: A figure window opens displaying a bar chart.")
Output: A figure window opens displaying a bar chart.
#85.
plt.boxplot()Makes a box and whisker plot.
import matplotlib.pyplot as plt
import numpy as np
data = [np.random.normal(0, std, 100) for std in range(1, 4)]
plt.boxplot(data)
print("Output: A figure window opens displaying a box plot.")
Output: A figure window opens displaying a box plot.
#86.
sns.heatmap()Plots rectangular data as a color-encoded matrix.
import seaborn as sns
import numpy as np
data = np.random.rand(10, 12)
sns.heatmap(data)
print("Output: A figure window opens displaying a heatmap.")
Output: A figure window opens displaying a heatmap.
#87.
sns.pairplot()Plots pairwise relationships in a dataset.
❤3
import seaborn as sns
import pandas as pd
df = pd.DataFrame(np.random.randn(100, 4), columns=['A', 'B', 'C', 'D'])
# sns.pairplot(df) # This line would generate the plot
print("Output: A figure grid opens showing scatterplots for each pair of variables.")
Output: A figure grid opens showing scatterplots for each pair of variables.
#88.
sns.countplot()Shows the counts of observations in each categorical bin using bars.
import seaborn as sns
import pandas as pd
df = pd.DataFrame({'category': ['A', 'B', 'A', 'C', 'A', 'B']})
sns.countplot(x='category', data=df)
print("Output: A figure window opens showing a count plot.")
Output: A figure window opens showing a count plot.
#89.
sns.jointplot()Draws a plot of two variables with bivariate and univariate graphs.
import seaborn as sns
import pandas as pd
df = pd.DataFrame({'x': range(50), 'y': range(50) + np.random.randn(50)})
# sns.jointplot(x='x', y='y', data=df) # This line would generate the plot
print("Output: A figure shows a scatter plot with histograms for each axis.")
Output: A figure shows a scatter plot with histograms for each axis.
#90.
plt.show()Displays all open figures.
import matplotlib.pyplot as plt
plt.plot([1, 2, 3])
# plt.show() # In a script, this is essential to see the plot.
print("Executes the command to render and display the plot.")
Executes the command to render and display the plot.
---
#DataAnalysis #ScikitLearn #Modeling #Preprocessing
Part 9: Scikit-learn - Modeling & Preprocessing
#91.
train_test_split()Splits arrays or matrices into random train and test subsets.
from sklearn.model_selection import train_test_split
import numpy as np
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
print(f"X_train shape: {X_train.shape}")
print(f"X_test shape: {X_test.shape}")
X_train shape: (3, 2)
X_test shape: (2, 2)
#92.
StandardScaler()Standardizes features by removing the mean and scaling to unit variance.
from sklearn.preprocessing import StandardScaler
data = [[0, 0], [0, 0], [1, 1], [1, 1]]
scaler = StandardScaler()
print(scaler.fit_transform(data))
[[-1. -1.]
[-1. -1.]
[ 1. 1.]
[ 1. 1.]]
#93.
MinMaxScaler()Transforms features by scaling each feature to a given range, typically [0, 1].
from sklearn.preprocessing import MinMaxScaler
data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
scaler = MinMaxScaler()
print(scaler.fit_transform(data))
[[0. 0. ]
[0.25 0.25]
[0.5 0.5 ]
[1. 1. ]]
#94.
LabelEncoder()Encodes target labels with values between 0 and n_classes-1.
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
encoded = le.fit_transform(['paris', 'tokyo', 'paris'])
print(encoded)
[0 1 0]
#95.
OneHotEncoder()Encodes categorical features as a one-hot numeric array.
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
X = [['Male'], ['Female'], ['Female']]
print(enc.fit_transform(X).toarray())
[[0. 1.]
[1. 0.]
[1. 0.]]
#96.
LinearRegression()Ordinary least squares Linear Regression model.
from sklearn.linear_model import LinearRegression
X = [[0], [1], [2]]
y = [0, 1, 2]
reg = LinearRegression().fit(X, y)
print(f"Coefficient: {reg.coef_[0]}")
❤2