Joining array¶
In [6]:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.concatenate((arr1, arr2))
print(arr)
[1 2 3 4 5 6]
Stack Functions¶
In [7]:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.stack((arr1, arr2), axis=1)
print(arr)
[[1 4] [2 5] [3 6]]
Stacking Along Rows¶
In [8]:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.hstack((arr1, arr2))
print(arr)
[1 2 3 4 5 6]
Stacking Along Columns¶
In [9]:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.vstack((arr1, arr2))
print(arr)
[[1 2 3] [4 5 6]]
Height (depth)¶
In [11]:
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
arr = np.dstack((arr1, arr2))
print(arr)
[[[1 4] [2 5] [3 6]]]
Sorting Arrays¶
In [12]:
import numpy as np
arr = np.array([3, 2, 0, 1])
print(np.sort(arr))
[0 1 2 3]
Sorting a 2-D Array¶
In [13]:
import numpy as np
arr = np.array([[3, 2, 4], [5, 0, 1]])
print(np.sort(arr))
[[2 3 4] [0 1 5]]
Filtering Arrays¶
In [14]:
import numpy as np
arr = np.array([41, 42, 43, 44])
x = [True, False, True, False]
newarr = arr[x]
print(newarr)
[41 43]
Creating the Filter Array¶
In [15]:
import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = []
for element in arr:
if element > 42:
filter_arr.append(True)
else:
filter_arr.append(False)
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[False, False, True, True] [43 44]
Creating Filter Directly From Array¶
In [16]:
import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = arr > 42
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)
[False False True True] [43 44]
Random Number¶
from numpy import random
x = random.randint(100)
print(x)
Generate Random Float¶
In [18]:
from numpy import random
x = random.rand()
print(x)
0.9381820419596202
Generate Random Array¶
In [19]:
from numpy import random
x=random.randint(100, size=(5))
print(x)
[84 35 5 67 53]
Random Number From Array¶
In [20]:
from numpy import random
x = random.choice([3, 5, 7, 9])
print(x)
7
Random Distribution¶
In [27]:
from numpy import random
x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(80))
print(x)
[7 7 5 3 5 7 7 7 7 7 3 7 7 7 7 5 7 7 7 5 7 5 3 5 5 7 7 7 7 5 3 7 7 5 7 7 7 5 3 3 7 5 7 5 5 5 7 7 7 7 3 7 7 7 5 7 5 5 7 7 7 7 5 7 3 7 7 7 7 5 5 5 5 5 3 7 5 5 7 5]
Shuffling Arrays¶
In [28]:
from numpy import random
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
random.shuffle(arr)
print(arr)
[3 5 4 1 2]
Generating Permutation of Arrays¶
In [29]:
from numpy import random
import numpy as np
arr = np.array([1, 2, 3, 4, 5])
print(random.permutation(arr))
[1 5 4 2 3]
In [30]:
!pip install seaborn
Collecting seaborn Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB) Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\users\admin\anaconda3\envs\abhis-learning\lib\site-packages (from seaborn) (2.2.4) Collecting pandas>=1.2 (from seaborn) Downloading pandas-2.2.3-cp312-cp312-win_amd64.whl.metadata (19 kB) Collecting matplotlib!=3.6.1,>=3.4 (from seaborn) Downloading matplotlib-3.10.1-cp312-cp312-win_amd64.whl.metadata (11 kB) Collecting contourpy>=1.0.1 (from matplotlib!=3.6.1,>=3.4->seaborn) Downloading contourpy-1.3.1-cp312-cp312-win_amd64.whl.metadata (5.4 kB) Collecting cycler>=0.10 (from matplotlib!=3.6.1,>=3.4->seaborn) Using cached cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB) Collecting fonttools>=4.22.0 (from matplotlib!=3.6.1,>=3.4->seaborn) Downloading fonttools-4.56.0-cp312-cp312-win_amd64.whl.metadata (103 kB) Collecting kiwisolver>=1.3.1 (from matplotlib!=3.6.1,>=3.4->seaborn) Downloading kiwisolver-1.4.8-cp312-cp312-win_amd64.whl.metadata (6.3 kB) Requirement already satisfied: packaging>=20.0 in c:\users\admin\anaconda3\envs\abhis-learning\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.1) Collecting pillow>=8 (from matplotlib!=3.6.1,>=3.4->seaborn) Downloading pillow-11.1.0-cp312-cp312-win_amd64.whl.metadata (9.3 kB) Collecting pyparsing>=2.3.1 (from matplotlib!=3.6.1,>=3.4->seaborn) Downloading pyparsing-3.2.2-py3-none-any.whl.metadata (5.0 kB) Requirement already satisfied: python-dateutil>=2.7 in c:\users\admin\anaconda3\envs\abhis-learning\lib\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in c:\users\admin\anaconda3\envs\abhis-learning\lib\site-packages (from pandas>=1.2->seaborn) (2024.1) Collecting tzdata>=2022.7 (from pandas>=1.2->seaborn) Downloading tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB) Requirement already satisfied: six>=1.5 in c:\users\admin\anaconda3\envs\abhis-learning\lib\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0) Downloading seaborn-0.13.2-py3-none-any.whl (294 kB) Downloading matplotlib-3.10.1-cp312-cp312-win_amd64.whl (8.1 MB) ---------------------------------------- 0.0/8.1 MB ? eta -:--:-- ---------- ----------------------------- 2.1/8.1 MB 10.7 MB/s eta 0:00:01 ---------------------- ----------------- 4.5/8.1 MB 11.2 MB/s eta 0:00:01 --------------------------------- ------ 6.8/8.1 MB 11.3 MB/s eta 0:00:01 ---------------------------------------- 8.1/8.1 MB 10.2 MB/s eta 0:00:00 Downloading pandas-2.2.3-cp312-cp312-win_amd64.whl (11.5 MB) ---------------------------------------- 0.0/11.5 MB ? eta -:--:-- -------- ------------------------------- 2.4/11.5 MB 12.2 MB/s eta 0:00:01 ----------------- ---------------------- 5.0/11.5 MB 11.6 MB/s eta 0:00:01 ------------------------- -------------- 7.3/11.5 MB 11.9 MB/s eta 0:00:01 ---------------------------------- ----- 10.0/11.5 MB 11.9 MB/s eta 0:00:01 ---------------------------------------- 11.5/11.5 MB 11.2 MB/s eta 0:00:00 Downloading contourpy-1.3.1-cp312-cp312-win_amd64.whl (220 kB) Using cached cycler-0.12.1-py3-none-any.whl (8.3 kB) Downloading fonttools-4.56.0-cp312-cp312-win_amd64.whl (2.2 MB) ---------------------------------------- 0.0/2.2 MB ? eta -:--:-- -------------------------------------- - 2.1/2.2 MB 11.7 MB/s eta 0:00:01 ---------------------------------------- 2.2/2.2 MB 10.3 MB/s eta 0:00:00 Downloading kiwisolver-1.4.8-cp312-cp312-win_amd64.whl (71 kB) Downloading pillow-11.1.0-cp312-cp312-win_amd64.whl (2.6 MB) ---------------------------------------- 0.0/2.6 MB ? eta -:--:-- ------------------------------- -------- 2.1/2.6 MB 10.7 MB/s eta 0:00:01 ---------------------------------------- 2.6/2.6 MB 10.1 MB/s eta 0:00:00 Downloading pyparsing-3.2.2-py3-none-any.whl (111 kB) Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) Installing collected packages: tzdata, pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, pandas, matplotlib, seaborn Successfully installed contourpy-1.3.1 cycler-0.12.1 fonttools-4.56.0 kiwisolver-1.4.8 matplotlib-3.10.1 pandas-2.2.3 pillow-11.1.0 pyparsing-3.2.2 seaborn-0.13.2 tzdata-2025.2
Import Seaborn¶
In [31]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.distplot([0, 1, 2, 3, 4, 5])
plt.show()
C:\Users\Admin\AppData\Local\Temp\ipykernel_12220\1138594208.py:4: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot([0, 1, 2, 3, 4, 5])
Plotting a Distplot Without the Histogram¶
In [32]:
import matplotlib.pyplot as plt
import seaborn as sns
sns.distplot([0, 1, 2, 3, 4, 5], hist=False)
plt.show()
C:\Users\Admin\AppData\Local\Temp\ipykernel_12220\3434429482.py:4: UserWarning: `distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751 sns.distplot([0, 1, 2, 3, 4, 5], hist=False)
In [ ]: