text_html_output_1.png
import matplotlib.pyplot as plt

import nltk

import os

import pandas as pd

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression
import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
from mpl_toolkits.mplot3d import Axes3D
import tensorflow as tf
import tensorflow_datasets as tfds
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.feature_extraction.text import CountVectorizer
from nltk.corpus import stopwords
from collections import Counter
import nltk
nltk.download('stopwords')
import re
from collections import defaultdict
from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder, OrdinalEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline, FeatureUnion, make_pipeline
from xgboost import XGBRegressor
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from scipy import stats
from scipy.stats import norm, skew
from scipy.special import boxcox1p
from sklearn.preprocessing import RobustScaler
import os

medal = pd.read_excel('../input/2021-olympics-in-tokyo/Medals.xlsx', index_col=0)
athlete = pd.read_excel('../input/2021-olympics-in-tokyo/Athletes.xlsx', index_col=0)
gender = pd.read_excel('../input/2021-olympics-in-tokyo/EntriesGender.xlsx', index_col=0)
team = pd.read_excel('../input/2021-olympics-in-tokyo/Teams.xlsx', index_col=0)
coach = pd.read_excel('../input/2021-olympics-in-tokyo/Coaches.xlsx', index_col=0)

count1 = athlete['NOC'].value_counts().head(10)
sns.barplot(x=count1.index,y = count1.values)
plt.xticks(rotation = 90)

plt.show()

count = athlete['Discipline'].value_counts().head(10)
sns.barplot(x=count.index, y=count.values)
plt.xticks(rotation=90)
plt.show()