<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from google.colab import files
uploaded = files.upload()

data=pd.read_csv('1. Weather Data.csv')
data
data.shape
data.index
data.dtypes
data.count
data.nunique()
data.info()
data.head(2)
data[data.Weather == 'Clear']
data.head(3)
data.groupby('Weather').get_group('Clear')
data.head(3)
data[(data['Weather'] == 'Fog') & (data['Temp_C'] > -1)]
import matplotlib.pyplot as plt
plt.plot(data['Temp_C'])
plt.show()
plt.hist(data['Visibility_km'],bins=10,facecolor="red")
plt.show()

plt.stem(data['Rel Hum_%'])
plt.show()
data.to_html('Weather-Report')
pd.read_html('Weather-Report')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18093922651933703, 'fraction_numerical': 0.009668508287292817, 'mean_word_length': 7.238636363636363, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 6, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '257607', 'license': 'MIT', 'n_tokens_mistral': 293, 'n_tokens_neox': 283, 'n_words': 51, 'path': 'Weather.ipynb', 'repo_name': 'Nikunjmistry22/Data_Analysis', 'size': '724'}