text_html_output_1.png
import numpy as np

import pandas as pd

app_data = pd.read_csv('../input/googleplaystore.csv')

app_data.isnull().sum()

app_data = app_data.sort_values(by='Installs', ascending=False)

app_data = app_data.sort_values(by='Installs')

nan_rows = list(app_data[app_data['Rating'].isna()].index)
nan_rows.append(10472)
app_data = app_data.drop(nan_rows, axis=0)
app_data = app_data.drop(columns=['Size', 'Current Ver', 'Android Ver'])
app_data = app_data.sort_values(by='Installs', ascending=False)

top_10 = np.unique(app_data['Installs'])
top_10 = sorted(top_10, key=len, reverse=True)[:10]
del top_10

installs = [np.int(i.replace('+', '').replace(',', '')) for i in app_data['Installs']]
app_data['Installs'] = [i for i in installs]
del installs

top_10_df = app_data[app_data['Installs'] >= 50000]

most_reviewed = pd.DataFrame()
for bins in np.unique(top_10_df['Installs']):
    top_row = top_10_df[top_10_df['Installs'] == bins]
    top_row = top_row.sort_values(by='ReviewRatio', ascending=False)
    top_row = top_row.head(1)
    most_reviewed = most_reviewed.append(top_row)
most_reviewed = most_reviewed.drop(columns=['Category', 'Type', 'Price', 'Content Rating', 'Genres', 'Last Updated'])
most_reviewed