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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 In this notebook, I am exploring the Meta Kaggle data (https
Step2:  Is there any relationship between user ranking and the number of competitions entered?
Let us explore this by plotting the rank vs. number of competitions entered for all users. First, we need a table with these 2 fields. We can construct this by joining the Teams and TeamMemberships tables. 
Pandas has a function read_sql_query to execute a SQL query. The output is a data frame with the column names corresponding to sql-table column names.
Step4:  We have used the log-log scale for both the x- and y-axes so that we could see more clearly the standing of top-ranking individuals. From this plot, it looked like practice contributes to user standing.
Do competitions winners work individually or collaboratively?
Let us explore the sizes of all teams that ever took the 1st place in a competition. To do this, we construct a table with team and team size as its fields using the Teams and TeamMemberships tables.
Step6:  So, most winning teams consisted of only one individual
Explore the number of registrations by time
Let us plot the number of users by their registration date month-by-month. The data is available in the Users table. We could do this by using SQL queries, but this time, I will do this by manipulating the pandas dataframe. Note the difference in the dataframe returned from sqlite3 and that returned by pd.read_csv() 
Get a dataframe from the SQLite Users table 
Step7:  Compare the previous dataframe with that constructed from the CSV file
Step8:  Note that while users_csv indicated columns with missing values, users_sqlite did not. It looked like pd.read_sql_query fills missing values with an empty string (i.e., ""). The RegisterDate column, however, seems to be complete. In the meantime, let us proceed with our exploration.
First, let us construct another column 'MonYear' which stores only the month and year of registration date
Step9:  Plot the number of new users vs. month/year of registration
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Python Code:
import pandas as pd
import sqlite3
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
cnx = sqlite3.connect('database.sqlite')
Explanation: In this notebook, I am exploring the Meta Kaggle data (https://www.kaggle.com/c/meta-kaggle). The dataset is available in both csv and sqlite database formats.
There are 10 tables:
* Users
* Teams
* TeamMemberships
* Competitions
* Submissions
* CopetitionHostSegments
* RewardTypes
* Sites
* ValidationSets
* EvaluationAlgorithms
Getting started: import modules and connect to the SQLite database
End of explanation
rank_vs_ncomps = pd.read_sql_query(
SELECT U.Ranking AS rank, COUNT(M.TeamId) as num_comps
FROM Users AS U 
JOIN TeamMemberships AS M ON U.Id = M.UserId 
GROUP BY U.Id, U.Ranking
, cnx) 
ax = rank_vs_ncomps.plot(x='rank', y='num_comps',   
        logx=True, logy= True, 
        kind='scatter')
ax.set_xlabel('log10(User Rank)', fontsize=14)
ax.set_ylabel('log10(# competitions entered)', fontsize = 14)
Explanation: Is there any relationship between user ranking and the number of competitions entered?
Let us explore this by plotting the rank vs. number of competitions entered for all users. First, we need a table with these 2 fields. We can construct this by joining the Teams and TeamMemberships tables. 
Pandas has a function read_sql_query to execute a SQL query. The output is a data frame with the column names corresponding to sql-table column names.
End of explanation
team_top3 = pd.read_sql_query(
SELECT team_size, rank, COUNT(*) AS num_teams
FROM
(    
    SELECT T.Id AS team_id, T.Ranking AS rank, COUNT(M.UserId) AS team_size
    FROM Teams AS T
    JOIN TeamMemberships AS M ON T.Id = M.TeamId
    WHERE T.Ranking IN (1, 2, 3)
    GROUP BY T.Id, T.Ranking
)
GROUP BY team_size, rank
, cnx)
team_top3['team_size'] = team_top3['team_size'].map(lambda x : x if (x<5) else 5)
team_top3 = team_top3.groupby(['team_size', 'rank'], as_index=False).sum()
ax = team_top3[team_top3['rank'] == 1].plot(kind='bar', 
                                       x='team_size',
                                       y='num_teams',
                                      title='Size of winning (1st place) teams')
ax.set_xticklabels(['1','2','3','4','>=5'], rotation='horizontal')
ax.set_xlabel('Team Size', fontsize=14)
ax.set_ylabel('Number of Teams', fontsize = 14)
Explanation: We have used the log-log scale for both the x- and y-axes so that we could see more clearly the standing of top-ranking individuals. From this plot, it looked like practice contributes to user standing.
Do competitions winners work individually or collaboratively?
Let us explore the sizes of all teams that ever took the 1st place in a competition. To do this, we construct a table with team and team size as its fields using the Teams and TeamMemberships tables.
End of explanation
users_sqlite = pd.read_sql_query(
SELECT *
FROM Users
, cnx)
users_sqlite.info()
Explanation: So, most winning teams consisted of only one individual
Explore the number of registrations by time
Let us plot the number of users by their registration date month-by-month. The data is available in the Users table. We could do this by using SQL queries, but this time, I will do this by manipulating the pandas dataframe. Note the difference in the dataframe returned from sqlite3 and that returned by pd.read_csv() 
Get a dataframe from the SQLite Users table :
End of explanation
users_csv = pd.read_csv('Users.csv', header=0)
users_csv.info()
Explanation: Compare the previous dataframe with that constructed from the CSV file:
End of explanation
import re         # a regexp module
users_csv['MonYear'] = users_csv['RegisterDate'].map(lambda x : re.sub(r'([0-9]{4})-([0-9]{2})-([0-9]{2}).*', 
                                                                       r'\1\2', x))
# Get the count of registered users month-by-month
reg_by_monyear = users_csv.groupby('MonYear', as_index=False)['MonYear'].agg({'num' : 'count'})
# From MonYear, construct a column of datetime object called 'Time' - Set the day in the date with 01
reg_by_monyear['Time'] = reg_by_monyear['MonYear'].map(lambda x:
                                             pd.to_datetime(x + '01', format='%Y%m%d'))
Explanation: Note that while users_csv indicated columns with missing values, users_sqlite did not. It looked like pd.read_sql_query fills missing values with an empty string (i.e., ""). The RegisterDate column, however, seems to be complete. In the meantime, let us proceed with our exploration.
First, let us construct another column 'MonYear' which stores only the month and year of registration date:
End of explanation
ax = reg_by_monyear.plot(x='Time', y='num', 
                    title = 'Number of new registrations by time',
                    figsize=(8,4))
ax.set_xlabel('Month/Year of Registration', fontsize=14)
ax.set_ylabel('Number of registrants', fontsize = 14)
Explanation: Plot the number of new users vs. month/year of registration:
End of explanation
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