<jupyter_start><jupyter_text><jupyter_code>from google.colab import drive
drive.mount("/content/drive")
# import modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# reading data
main_data = pd.read_csv('/content/drive/MyDrive/Sem7/My_ML/Lab1/Data_for_Transformation.csv')
# printing head and scatter plot
print(main_data.head())
plt.scatter(main_data["Age"], main_data["Salary"])
plt.show()
# 2nd plot
plt.hist(main_data["Salary"], bins = 10, color = "yellow")
plt.show()
# 3rd plot
fig_size = plt.figure(figsize=(7, 5))
plt.bar(main_data["Country"], main_data["Salary"], color="green")
plt.xlabel("Salaries")
plt.ylabel("Countries")
plt.title("Bar chart of country vs salary")
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.15528781793842034, 'fraction_numerical': 0.0107095046854083, 'mean_word_length': 7.21978021978022, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '228263', 'license': 'MIT', 'n_tokens_mistral': 285, 'n_tokens_neox': 266, 'n_words': 57, 'path': 'Lab1/Lab1_Pandas.ipynb', 'repo_name': 'jay281/014_JayBhingradiya', 'size': '747'}