Aggregating Data

Mean

Mean is the average of the given numbers and is calculated by dividing the sum of given numbers by the total number of numbers.

Median

The Median is the Middle value in the list of numbers.

Data

In [30]:

import pandas as pd
import numpy as np
walmart = pd.read_csv('walmart.csv')
walmart['Date']  = pd.to_datetime(walmart['Date'])
walmart.head()

Out[30]:

	Store	Type	Department	Weekly_Sales	Is_Holiday	Temperature	Fuel_Price	Unemployment	Date
0	1	A	1	57258.43	False	62.27	2.719	7.808	2010-04-02
1	1	A	1	16333.14	False	80.91	2.669	7.787	2010-07-02
2	1	A	1	19403.54	False	46.63	2.561	8.106	2010-02-26
3	1	A	1	22517.56	False	49.27	2.708	7.838	2010-12-03
4	1	A	1	17596.96	False	66.32	2.808	7.808	2010-04-16

So, this is our Data about Walmart store. In this module we are going to use Walmart DataSet.

Let's find out the the mean and median for Weekly_Sales column.

In [31]:

walmart['Weekly_Sales'].mean()

Out[31]:

15983.4296920532

In [32]:

walmart['Weekly_Sales'].median()

Out[32]:

7616.55

Summarizing dates

Maximum and Minimum allow us to see what time range your data covers.

In [33]:

walmart['Date'].max()

Out[33]:

Timestamp('2012-10-26 00:00:00')

In [34]:

walmart['Date'].min()

Out[34]:

Timestamp('2010-02-05 00:00:00')

By using .max() and .min() we get to know that our DataSet have data from January of 2010 to December of 2012.

Efficient Summaries

Agg Method:

The .agg() method allows you to apply your own custom functions to a DataFrame, as well as apply functions to more than one column of a DataFrame at once, making your aggregations super efficient.

Have you noticed that our temperature column is in Fahrenheit and I want to convert it into Celsius. Now, we will make my custom function that will convert temperature from Fahrenheit to Celsius and we will apply it through .agg() method to a DataFrame column.

In [35]:

def f_to_c(column):
    return (column-32)*5/9

walmart['Temperature'].agg(f_to_c)

Out[35]:

0         16.816667
1         27.172222
2          8.127778
3          9.594444
4         19.066667
            ...    
282446    18.511111
282447    18.266667
282448     2.777778
282449    25.916667
282450    16.244444
Name: Temperature, Length: 282451, dtype: float64

Here, we are applying a custom function with the help of .agg() method, you can pass the list of functions to apply more than one function or you can pass the list of columns if you want to apply the function on more than one column.

Cumulative Statistics

Cumulative statistics can also be helpful in tracking summary statistics over time. We'll calculate the cumulative sum and cumulative max of a department's weekly sales, which will allow us to identify what the total sales were so far as well as what the highest weekly sales were so far.

Cumulative sum of weekly_sales

In [36]:

# cum_walmart['Cum_Weekly_Sales'] = cum_walmart['Weekly_Sales'].cumsum()

Dropping duplicates

Removing duplicates is very essential to get accurate counts, because we don't want to count the same thing multiple times. In this exercise, we'll create some new DataFrames using unique values from walmart.

In [37]:

walmart_store_types = walmart.drop_duplicates(subset=['Store','Type'])
walmart_store_types.head()

Out[37]:

	Store	Type	Department	Weekly_Sales	Is_Holiday	Temperature	Fuel_Price	Unemployment	Date
0	1	A	1	57258.43	False	62.27	2.719	7.808	2010-04-02
6946	2	A	1	27023.35	False	69.24	2.603	8.163	2010-10-01
13804	3	B	1	4238.56	False	82.20	2.669	7.346	2010-07-02
19897	4	A	1	36486.28	False	63.96	2.619	7.127	2010-10-01
26713	5	B	1	8588.14	False	71.10	2.603	6.768	2010-10-01

Counting categorical variables

Counting is a great way to get an overview of your data and to spot unknown information that you might not notice. We'll count the number of each type of store and the number of each department number using the DataFrames we created in Dropping Duplicates.

Counting the number of stores of each Type

In [38]:

walmart_store_counts = walmart_store_types['Type'].value_counts()
walmart_store_counts

Out[38]:

A    22
B    17
C     6
Name: Type, dtype: int64

Proportion of stores of each Type

Normalize: normalize argument is used to turn the counts into proportion of the total.

In [39]:

walmart_store_props = walmart_store_types['Type'].value_counts(normalize=True)
walmart_store_props

Out[39]:

A    0.488889
B    0.377778
C    0.133333
Name: Type, dtype: float64

Counting the number of each department number and sorting values

In [40]:

walmart_dept_counts_sorted = walmart['Department'].value_counts(sort=True)
walmart_dept_counts_sorted

Out[40]:

90    4394
2     4379
79    4360
91    4355
14    4352
      ... 
78     147
65      96
77      93
39       9
43       8
Name: Department, Length: 81, dtype: int64

Proportion of departments of each number and sorting values

In [41]:

walmart_dept_props_sorted = walmart['Department'].value_counts(sort=True, normalize=True)
walmart_dept_props_sorted

Out[41]:

90    0.015557
2     0.015504
79    0.015436
91    0.015419
14    0.015408
        ...   
78    0.000520
65    0.000340
77    0.000329
39    0.000032
43    0.000028
Name: Department, Length: 81, dtype: float64

.groupby()

Pandas groupby is used for grouping the data according to the categories and apply a function to the categories. It also helps to aggregate data efficiently.

Group by Type to calculate total Weekly_Sales

In [42]:

walmart_sales_by_type = walmart.groupby("Type")["Weekly_Sales"].sum()
walmart_sales_by_type

Out[42]:

Type
A    2.902625e+09
B    1.341802e+09
C    2.701086e+08
Name: Weekly_Sales, dtype: float64

Here, we group by the Type and perform sum on the column Weekly_Sales.

Group by Type and Is_Holiday to calculate total Weekly_Sales

In [43]:

walmart_sales_by_type_is_holiday = walmart.groupby(["Type",'Is_Holiday'])["Weekly_Sales"].sum()
walmart_sales_by_type_is_holiday

Out[43]:

Type  Is_Holiday
A     False         2.689482e+09
      True          2.131421e+08
B     False         1.237606e+09
      True          1.041968e+08
C     False         2.507886e+08
      True          1.931996e+07
Name: Weekly_Sales, dtype: float64

In this case we are grouping by two columns and summarizing one column if you want to perfrom any other summary statistics on more than one column then you have to pass the list of those column.

Multiple grouped summaries

Earlier we saw that the .agg() method is useful to compute multiple statistics on multiple variables. It also works with .groupby or grouped data.

For each store Type, aggregating Weekly_Sales to gett min, max, mean, and median

In [44]:

walmart_sales_stats = walmart.groupby('Type')['Weekly_Sales'].agg([np.min,np.max,np.mean,np.median])
walmart_sales_stats

Out[44]:

	amin	amax	mean	median
Type
A	-4988.94	381072.11	20079.169258	10096.75
B	-1750.00	693099.36	12263.647825	6197.43
C	-379.00	112152.35	9484.482310	1137.34

Notice that the minimum Weekly_Sales is negative because some stores had more returns than sales.

For each store Type, aggregating Unemployment and Fuel_Price to get min, max, mean, and median

In [45]:

walmart_unemp_fuel_stats = walmart.groupby('Type')[['Unemployment','Fuel_Price']].agg([np.min,np.max,np.mean,np.median])
walmart_unemp_fuel_stats

Out[45]:

	Unemployment	Fuel_Price
	amin	amax	mean	median	amin	amax	mean	median
Type
A	3.879	14.313	7.798863	7.818	2.472	4.468	3.343295	3.416
B	4.125	14.313	7.936579	7.874	2.514	4.468	3.381915	3.494
C	5.217	14.313	8.948222	8.300	2.514	4.468	3.363569	3.417

Pivot tables

Pivot tables are another way of calculating grouped summary statistics. Basically pivot tables are used in spreadsheets, if you have ever used spreadsheets then chances are you have used pivot tables. Let's see how to create pivot tables in pandas.

Pivoting on one variable

We'll perform calculations using .pivot_table() to replicate the calculations we performed in the .groupby().

Pivoting for mean Weekly_Sales for each store Type

In [46]:

mean_sales_by_type = walmart.pivot_table(values="Weekly_Sales", index="Type")
mean_sales_by_type

Out[46]:

	Weekly_Sales
Type
A	20079.169258
B	12263.647825
C	9484.482310

Pivoting for mean and median Weekly_Sales for each store Type

In [47]:

mean_med_sales_by_type = walmart.pivot_table(values="Weekly_Sales", index="Type", aggfunc=[np.mean,np.median])
mean_med_sales_by_type

Out[47]:

	mean	median
	Weekly_Sales	Weekly_Sales
Type
A	20079.169258	10096.75
B	12263.647825	6197.43
C	9484.482310	1137.34

Pivoting for mean Seekly_Sales by store Type and Holiday

In [48]:

mean_sales_by_type_holiday = walmart.pivot_table(values="Weekly_Sales", index="Type", columns='Is_Holiday')
mean_sales_by_type_holiday

Out[48]:

Is_Holiday	False	True
Type
A	20009.541574	21001.295283
B	12166.429612	13549.646294
C	9464.078381	9757.554889

Fill in missing values and sum values with pivot tables

.pivot_table() method has several useful arguments, including fill_value and margins.

fill_value: replaces missing values with real value. margins: is a shortcut for when we pivoted by two variables, but also wanted to pivot by each of those variables separately, it gives the row and column totals of the pivot table contents.

mean of Weekly_Sales by Department and Type, fill missing values with 0

In [49]:

walmart.pivot_table(values='Weekly_Sales', index='Department', columns='Type', fill_value=0)

Out[49]:

Type	A	B	C
Department
1	23280.657103	17821.729825	8955.170884
2	51935.206120	43359.816952	14398.908941
3	14044.489715	12880.306941	803.546661
4	33128.245264	21457.823086	13506.047051
5	26589.677249	21109.894917	766.519121
...	...	...	...
95	97473.593714	41428.486751	50990.968371
96	19771.434235	4874.171608	15720.388253
97	21937.066379	3726.370891	13468.309357
98	10971.473930	317.638051	5406.640087
99	486.289516	21.008889	3.332143

81 rows × 3 columns

mean Weekly_Sales by Department and Type, fill missing values with 0s, sum all rows and cols

In [50]:

walmart.pivot_table(values="Weekly_Sales", index="Department", columns="Type", fill_value=0, margins=True)

Out[50]:

Type	A	B	C	All
Department
1	23280.657103	17821.729825	8955.170884	19304.958040
2	51935.206120	43359.816952	14398.908941	43558.699406
3	14044.489715	12880.306941	803.546661	11880.837545
4	33128.245264	21457.823086	13506.047051	26111.912583
5	26589.677249	21109.894917	766.519121	21218.756123
...	...	...	...	...
96	19771.434235	4874.171608	15720.388253	15212.524956
97	21937.066379	3726.370891	13468.309357	14291.078869
98	10971.473930	317.638051	5406.640087	6804.735435
99	486.289516	21.008889	3.332143	468.071664
All	20079.169258	12263.647825	9484.482310	15983.429692

82 rows × 4 columns