11: 00 am vizualizatsiy ipynb Colaboratory

age year
0
10 2010
1
20 2000
2
30 1990
3
40 1980
4
50 1970
5
60 1960
# yuqoridagi DF da manfiy bog'liqlik bor
import pandas as pd
from pandas import DataFrame
df=pd.DataFrame(data)
df

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2/23/23, 11:00 AM
Vizualizatsiya2.ipynb - Colaboratory
https://colab.research.google.com/drive/1JW1m0PA5HiH-RnrL_HjQNTzlxW24yQpM#scrollTo=Y3XJ7TyxfGjo&printMode=true
5/7
df.age.corr(df.year)
-0.9999999999999998
year
price
mileage
tax
mpg engineSize
year
1.000000
0.520712 -0.738027
0.012480 -0.094626
-0.142147
price
0.520712
1.000000 -0.537214
0.268717 -0.438445
0.516126
mileage
-0.738027 -0.537214
1.000000 -0.160223
0.202850
0.063652
tax
0.012480
0.268717 -0.160223
1.000000 -0.513742
0.338341
mpg
-0.094626 -0.438445
0.202850 -0.513742
1.000000
-0.339862
engineSize
-0.142147
0.516126
0.063652
0.338341 -0.339862
1.000000
# keling endi bu karillatsiyani dfx ning ustunlari o'rtasida hisoblab ko'ramiz
# masalan year va milage ustunlari o'rtasida bog'liqlik bor
df1=pd.read_csv("/content/Ai-Fayls/merc.csv")
df1.corr()
# keling ikkita ustun o'rtasidagi korrelatsiyani aniqlab ko'ramiz
df1.corrwith(df1["price"])
year 0.520712
price 1.000000
mileage -0.537214
tax 0.268717
mpg -0.438445
engineSize 0.516126
dtype: float64
# keling endi barcha ustunlarning bir biri bilan karrilatsiyasini aniqlab ko'ramiz.
# endi esa barcha ustunlarni biror ustunga nisbatan solishtiramiz
# bunda corrwidth() metodidan foydalanamiz
Tarqoqlik diagrammasi
Scatter plot yordamida ma'lumotlar qanchalik tarqoq ekanin kuzatishimiz mumkin. Bu grafik ham bir nechta ustunlar orasidagi
bog'liqlikni (korrelyasiya) ko'rishga xizmat qiladi.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns # agar seaborn o'rnatilmagan bo'lsa pip install seaborn
Darsimiz uchun Mercedes-Benz firmasi avtomobillari narxini jamlagan jadvalni yuklab olamiz.
df=pd.read_csv("/content/Ai-Fayls/merc.csv")
df.head()

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2/23/23, 11:00 AM
Vizualizatsiya2.ipynb - Colaboratory
https://colab.research.google.com/drive/1JW1m0PA5HiH-RnrL_HjQNTzlxW24yQpM#scrollTo=Y3XJ7TyxfGjo&printMode=true
6/7