<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns
%matplotlib inline
from scipy import stats
from scipy.stats import norm
import math
sns.set()
weather=pd.read_csv('weather_2012.csv')
train=pd.read_csv('train.csv')
weather.head()
train
sns.heatmap(weather.isnull(),yticklabels=False,cbar=False,cmap='Blues')
plt.figure(figsize = (10,8))
sns.heatmap(train.isnull(), yticklabels = False, cbar = False, cmap = 'Blues', xticklabels = False)
np.random.seed(6)
mean = train['LotArea'].sample(n = 600).mean()
m2 = np.random.choice(train['LotArea'], size = 600)
mean
m2.mean()
mean_lotarea = train['LotArea'].mean()
mean_lotarea
np.random.seed(6)
sample_size = 2000
c = np.random.choice(weather['Temp (C)'], size = 2000)
arr = np.random.randn(10)
arr2 = np.linspace(1,10,10)
arr
y = stats.norm().cdf(arr2)
plt.plot(arr2,y)<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '93799', 'license': 'MIT', 'n_tokens_mistral': 400, 'n_tokens_neox': 369, 'n_words': 74, 'path': 'pract_day_10.ipynb', 'repo_name': 'Abhishekauti21/dsmp-pre-work', 'size': '932'}