#!/usr/bin/env python
# coding: utf-8

# # Grouping
# 
# A common need is bound to arise where you will need to look at an aggregate view of a `DataFrame` by a certain value.  This is where grouping comes in.
# 
# CashBox has asked that we produce a list of the top 10 users who have been on the receiving side of transactions the most. They would like to see the user's first and last name, their email, and the total number of transactions where the user was the receiver.
# 
# We can acheive this by grouping our data. We need to take a look at the **`transactions`** `DataFrame` and group it by the **`receiver`** field, which is the username.
# 
# Grouping on a value returns a new type of object called the [`GroupBy`](https://pandas.pydata.org/pandas-docs/stable/api.html#groupby).
# 
# Let's first explore how to create one of these, and then how to wield it's power.

# In[1]:


# Setup
import os

import pandas as pd

pd.options.display.max_rows = 10
users = pd.read_csv(os.path.join('data', 'users.csv'), index_col=0)
transactions = pd.read_csv(os.path.join('data', 'transactions.csv'), index_col=0)
# Sanity check
(len(users), len(transactions))


# Let's remind ourselves about the types of data we have in the **`transactions`** `DataFrame`.

# In[2]:


transactions.dtypes


# Grouping by a specific column is pretty straight forward. We want to group by the receiver, so we use the [`DataFrame.groupby`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html) method.

# In[3]:


grouped_by_receiver = transactions.groupby('receiver')

# Let's see what type of object we got back
type(grouped_by_receiver)


# We received a [`DataFrameGroupBy`](https://pandas.pydata.org/pandas-docs/stable/api.html#groupby) object. There are quite a few methods here.
# 
# Let's take a look first at [`GroupBy.size`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.size.html). This will return a `Series` of how many members are in each of the groups. In our case this is the number of transactions that each user received.

# In[4]:


# Returns a Series of total number of rows
grouped_by_receiver.size()


# Similarly, we can use the `DataFrameGroupBy.count` method to see counts of how many non missing data points we have across each column in our group across the columns of our `DataFrame`.

# In[5]:


grouped_by_receiver.count()


# The `GroupBy` object provides aggregate functions that makes getting calculations quick and seamless. For instance, if we use the `GroupBy.sum` method we can see each numeric column summed up for each grouping. In our case there is only one numeric column **`amount`**.

# In[6]:


grouped_by_receiver.sum()


# Now where were we? Oh right, we're trying to figure out the people who was received the most transactions.  So why don't we use this group to create a new column on our **`users`** `DataFrame`.

# In[7]:


# Create a new column in users called transaction count, and set the values to the size of the matching group
users['transaction_count'] = grouped_by_receiver.size()

# Not every user has made a transaction, let's see what kind of missing data we are dealing with
len(users[users.transaction_count.isna()])


# Since we don't have a transaction record for everyone, not every user will be in our grouping.  So when we created the new column, we ended up adding some `np.nan`.  Let's fix that.

# In[8]:


# Set all missing data to 0, since in reality, there have been 0 received transactions for this user
users.transaction_count.fillna(0, inplace=True)
users


# Check it out! There's our column, but it's a floating point number, we don't need that.  Let's convert it!

# In[9]:


# Convert from the default type of float64 to int64 (no precision needed)
users.transaction_count = users.transaction_count.astype('int64')


# Finally we want to get the user with the highest transaction count, so let's sort by that descending.

# In[10]:


# Sort our values by the new field descending (so the largest comes first), and then by first name ascending
users.sort_values(
    ['transaction_count', 'first_name'],
    ascending=[False, True],
    inplace=True
)
# Take a look at our top 10 receivers, showing only the columns we want
users.loc[:, ['first_name', 'last_name', 'email', 'transaction_count']].head(10)


# Here they are, the Top 10 Receivers!  Nice work putting all those skills together!