#data management and visualization

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Assignment 4: Creating Graphs for Your Data

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Assignment 1.

Assignment 2.

Assignment 3.

Link to download the dataset here.

Link to download the codebook here.

WHAT TO SUBMIT:

Once you have written a successful program that creates univariate and bivariate graphs, create a blog entry where you post your program and the graphs that you have created. Write a few sentences describing what your graphs reveal in terms of your individual variables and the relationship between them.

Download the graph program here.

In the last assignment (3), I had made the data management that I thought necessary. Now is time to create the graphics that represent this data.

I did that in two ways, in the first one I made the Quantitative->Quantitave method generating a scatterplot and the second one was a Qualitative->Quantitative method that creates a bar graph. Before I present the result of the relationship between the two variables in the graph, let’s see the histogram and the metrics extracted in each attribute separated.

Univariate graphs

Incidence of breast cancer

The first attribute was the incidence of breast cancer in 100,000 female residents during the 2002 year. As it is a quantitative attribute, was generated the histogram of the data. #Univariate histogram of the incidence of breast cancer in 100,000 female residents during the 2002 year.seaborn.distplot(sub1["breastCancer100th"].dropna(), kde=False);plt.xlabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.ylabel('Number of counties.')plt.title('Histogram of the Incidence of Breast Cancer.')plt.show()

We can observe in the histogram that most of the countries have an incidence of cancer around 30 and 40 cases per 100,000 females. The extracted metrics of this attribute were: desc1 = sub1["breastCancer100th"].describe()print(desc1) count    129.000000mean      37.987597std       24.323873min        3.90000025%       20.60000050%       29.70000075%       50.300000max      101.100000Name: breastCancer100th, dtype: float64

With this, we can see that 75% of the countries have an incidence of breast cancer under 50.30 per 100,000 females.

Sugar consumption

The second attribute is the sugar consumption. For this attribute, I have made two graphs: one that shows the histogram of the original data and the other one that shows the bar graph of this attribute relocated into categories.

Histogram

#Univariate histogram of the Mean of the sugar consumption (grams per person and day) between 1961 and 2002.seaborn.distplot(sub1["meanSugarPerson"].dropna(), kde=False);plt.xlabel('Mean of the sugar consumption (grams per person and day) between 1961 and 2002.')plt.ylabel('Number of counties.')plt.title('Histogram of the Sugar Consumption.')plt.show()

This histogram is almost evenly distributed, we can see that the countries that have the most sugar consumption are in the 20 and the 110 grams per person. desc2 = sub1["meanSugarPerson"].describe()print(desc2) count    129.000000mean      76.238394std       42.488004min        6.13238125%       42.20642950%       79.71452475%      110.307619max      163.861429Name: meanSugarPerson, dtype: float64

The mean of sugar consumption is 76.24 and we can see that 75% of the countries have a consumption of sugar under 110.31 grams per day.

Bar graph

#Univariate bar graph of the Mean of the sugar consumption (grams per person and day) between 1961 and 2002.seaborn.countplot(x="sugar_consumption", data=sub1)plt.xlabel('Mean of the sugar consumption (grams per person and day) between 1961 and 2002.')plt.ylabel('Number of counties.')plt.title('Histogram of the Sugar Consumption.')plt.show()

Where the consumption is:

(0) Desirable between 0 and 30 g.

(1) Raised between 30 and 60 g.

(2) Borderline high between 60 and 90 g.

(3) High between 90 and 120 g.

(4) Very high under 120g.

The bar graph behaved very similarly to the histogram.

Bivariate graphs

The two bivariate graphics are presented below: #Bivariate Scatterplot Q->Q - Incidence of breast cancer versus sugar consumptionscat1 = seaborn.regplot(x="meanSugarPerson", y="breastCancer100th", fit_reg=True, data=sub1)plt.xlabel('Mean of the sugar consumption between 1961 and 2002.')plt.ylabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.title('Scatterplot for the association between the incidence of breast cancer and the sugar consumption.')plt.show() #Bivariate bar graph C->Q - Incidence of breast cancer versus sugar consumptionseaborn.factorplot(x='sugar_consumption', y='breastCancer100th', data=sub1, kind="bar", ci=None)plt.xlabel('Mean of the sugar consumption between 1961 and 2002.')plt.ylabel('Incidence of breast cancer in 100,000 female residents during the 2002 year.')plt.title('Bar graph for the Association between the incidence of breast cancer and the sugar consumption.')plt.show()

In both graphics, we can see that there is a relation with the incidence of breast cancer and the consumption of sugar. While sugar consumption is increased the incidence of new breast cancer cases is increased too.

Review criteria

Your assessment will be based on the evidence you provide that you have completed all the steps. When relevant, gradients in the scoring will be available to reward clarity (for example, you will get one point for submitting graphs that do not accurately represent your data, but two points if the data is accurately represented). In all cases, consider that the peer assessing your work is likely not an expert in the field you are analyzing. You will be assessed equally in your description of your frequency distributions.

Specific rubric items, and their point values, are as follows:

Was a univariate graph created for each of the selected variables? (2 points)

Was a bivariate graph created for the selected variables? (2 points)

Did the summary describe what the graphs revealed in terms of the individual variables and the relationship between them? (2 points)

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Week 4

Creating graphs for your data code

import pandas import numpy import pandas as pd import seaborn import matplotlib.pyplot as plt

data = pd.read_csv('gapminder_pds.csv', low_memory=False)

bindata = data.copy()

convert variables to numeric format using convert_objects function

data['internetuserate'] = pd.to_numeric(data['internetuserate'], errors='coerce') bindata['internetuserate'] = pd.cut(data.internetuserate, 10)

data['incomeperperson'] = pd.to_numeric(data['incomeperperson'], errors='coerce') bindata['incomeperperson'] = pd.cut(data.incomeperperson, 10)

data['employrate'] = pd.to_numeric(data['employrate'], errors='coerce') bindata['employrate'] = pd.cut(data.employrate, 10)

data['femaleemployrate'] = pd.to_numeric(data['femaleemployrate'], errors='coerce') bindata['femaleemployrate'] = pd.cut(data.femaleemployrate, 10)

data['polityscore'] = pd.to_numeric(data['polityscore'], errors='coerce') bindata['polityscore'] = data['polityscore'] sub2 = bindata.copy()

Scatterplot for the Association Between Employment rate and lifeexpectancy

scat1 = seaborn.regplot(x="internetuserate", y="incomeperperson", fit_reg=False, data=data) plt.xlabel('Internet use rate') plt.ylabel('Income per person') plt.title('Scatterplot for the Association Between Internet use rate and Income per person')

This scatterplot show the relationship and seems to be exponential.

Univariate histogram for quantitative variable:

seaborn.distplot(data["incomeperperson"].dropna(), kde=False); plt.xlabel('Income per person')

The graph is highly right skewed. Incomes are small for most of the world and the wealthy tail is quite long.

Univariate histogram for quantitative variable:

seaborn.distplot(data["employrate"].dropna(), kde=False); plt.xlabel('Employ rate')

Summary

It looks like there are associations between Internet use rate and income per person going up with internet use rate and going up an an accelerating rate.

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[priyadarshinipanda]

Assignment 3: Making Data Management Decisions

WHAT TO SUBMIT:

Once you have written a successful program that manages your data, create a blog entry where you post your program and the results/output that displays at least 3 of your data managed variables as frequency distributions. Write a few sentences describing these frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.

Download the program here and the dataset here;

In the last assignment, I had already made the data management that I thought necessary, but I made it in the excel with formulas.

Now, I remade the data management directly in python, and the program output can be seen down in the post.

The results were still the same. The sample used was the incidence of new breast cancer cases in 129 different countries. After running the program, it was possible to observe that the consumption of sugar is considered desirable only in 20.9% of the countries of the dataset. Taking into account that this metric is based on the average of the desirable sugar ingest in grams per day of the woman (25g) and the man (36g) [1] and [2].

To the food consumption data, I made the average of all countries consumption and compared each country consumption to this mean. 55% of the countries stay under the average.

At last, to range the total cholesterol in the blood of the countries I used as a base the metric of Mayo Clinic [3]. In the dataset, none of the values exceeded to a high level of total cholesterol and almost 73% of the countries presented to be in the desirable level.

Reference

[1] Life by Daily Burn Are You Exceeding Your Daily Sugar Intake in Just One Meal INFOGRAPHIC. Visited 05 Jul 2016. URL: http://dailyburn.com/life/health/daily-sugar-intake-infographic/.

[2] MD-Health How Many Grams of Sugar Per Day. Visited 06/07/2016. URL: http://www.md-health.com/How-Many-Grams-Of-Sugar-Per-Day.html.

[3] Cholesterol Test - Procedure details. Visited 05 Jul 2016. URL: http://www.mayoclinic.org/tests-procedures/cholesterol-test/details/results/rsc-20169555.

Output of the program: Importing the packages and the data set (csv)

import pandasimport numpyimport statistics # Import the data set to memorydata = pandas.read_csv("separatedData.csv", low_memory = False) # Change data type among variables to numericdata["breastCancer100th"] = data["breastCancer100th"].convert_objects(convert_numeric=True)data["meanSugarPerson"]   = data["meanSugarPerson"].convert_objects(convert_numeric=True)data["meanFoodPerson"]    = data["meanFoodPerson"].convert_objects(convert_numeric=True)data["meanCholesterol"]   = data["meanCholesterol"].convert_objects(convert_numeric=True) # Create a subData with only the variables breastCancer100th, meanSugarPerson, meanFoodPerson, meanCholesterolsub1=data[['breastCancer100th','meanSugarPerson', 'meanFoodPerson', 'meanCholesterol']]

Making the new variable sugar_consumption

# Create the conditions to a new variable named sugar_consumption that will categorize the meanSugarPerson answersdef sugar_consumption (row):   if 0 < row['meanSugarPerson'] <= 30 : return 0    # Desirable between 0 and 30 g.   if 30 < row['meanSugarPerson'] <= 60 : return 1   # Raised between 30 and 60 g.   if 60 < row['meanSugarPerson'] <= 90 : return 2   # Borderline high between 60 and 90 g.   if 90 < row['meanSugarPerson'] <= 120 : return 3  # High between 90 and 120 g.   if row['meanSugarPerson'] > 120 : return 4        # Very high under 120g. # Add the new variable sugar_consumption to subDatasub1['sugar_consumption'] = sub1.apply (lambda row: sugar_consumption (row),axis=1) # Count of sugar_consumptionprint("Count of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 2002")c1 = sub1["sugar_consumption"].value_counts(sort=False)print(c1) # Percentage of sugar_consumptionprint("Percentage of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 2002")p1 = sub1["sugar_consumption"].value_counts(sort=False,normalize=True)print(p1)

1.1.1.2                                                              Count and Percentage of the new variable sugar_consumption

Count of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 20020    271    192    313    314    21Name: sugar_consumption, dtype: int64 Percentage of sugar_consumption - Range of sugar consumption based on the mean of the quantity (grams per person and day) of sugar and sweeters between 1961 and 20020    0.2093021    0.1472872    0.2403103    0.2403104    0.162791Name: sugar_consumption, dtype: float64

Making the new variable food_consumption

#Make the average of meanFoodPerson values.food_mean = statistics.mean(data["meanFoodPerson"]) # Create the conditions to a new variable named food_consumption that will categorize the meanFoodPerson answersdef food_consumption (row):   if row['meanFoodPerson'] <= food_mean : return 0  # Food consumption below the world average.   if row['meanFoodPerson'] > food_mean : return 1   # Food consumption under the world average. # Add the new variable food_consumption to subDatasub1['food_consumption'] = sub1.apply (lambda row: food_consumption (row),axis=1) # Count of food_consumptionprint("Count of food_consumption - Mean of the food consumption of countries based on the mean  of the total supply of food (kilocalories / person & day) between 1961 and 2002")c2 = sub1["food_consumption"].value_counts(sort=False)print(c2) # Percentage of food_consumptionprint("Percentage of food_consumption - Mean of the food consumption of countries based on the mean of the total supply of food (kilocalories / person & day) between 1961 and 2002")p2 = sub1["food_consumption"].value_counts(sort=False, normalize=True)print(p2)

Count and Percentage of the new variable food_consumption

Count of food_consumption - Mean of the food consumption of countries based on the mean  of the total supply of food (kilocalories / person & day) between 1961 and 20020    711    58Name: food_consumption, dtype: int64 Percentage of food_consumption - Mean of the food consumption of countries based on the mean of the total supply of food (kilocalories / person & day) between 1961 and 20020    0.5503881    0.449612Name: food_consumption, dtype: float64

Making the new variable cholesterol_blood

# Create the conditions to a new variable named cholesterol_blood that will categorize the meanCholesterol answersdef cholesterol_blood (row):   if row['meanCholesterol'] <= 5.2 : return 0         # Desirable below 5.2 mmol/L   if 5.2 < row['meanCholesterol'] <= 6.2 : return 1   # Borerline high between 5.2 and 6.2 mmol/L   if row['meanCholesterol'] > 6.2 : return 2          # High above 6.2 mmol/L # Add the new variable cholesterol_blood to subDatasub1['cholesterol_blood'] = sub1.apply (lambda row: cholesterol_blood (row),axis=1) # Count of cholesterol_bloodprint("Count of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 2002")c3 = sub1["cholesterol_blood"].value_counts(sort=False)print(c3) # Percentage of cholesterol_bloodprint("Percentage of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 2002")p3 = sub1["cholesterol_blood"].value_counts(sort=False, normalize=True)print(p3)

Count and Percentage of the new variable cholesterol_blood

Count of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 20020    941    35Name: cholesterol_blood, dtype: int64 Percentage of cholesterol_blood - Range of the average of the mean TC (Total Cholesterol) of the female population counted in mmol per L between 1980 and 20020    0.7286821    0.271318Name: cholesterol_blood, dtype: float64

Review criteria

Your assessment will be based on the evidence you provide that you have completed all the steps. When relevant, gradients in the scoring will be available to reward clarity (for example, you will get one point for submitting output that is not understandable, but two points if it is understandable). In all cases, consider that the peer assessing your work is likely not an expert in the field you are analyzing. You will be assessed equally in your description of your frequency distributions.

Specific rubric items, and their point values, are as follows:

Was the program output interpretable (i.e., organized and labelled)? (1 point)

Does the program output display three data managed variables as frequency tables? (1 point)

Did the summary describe the frequency distributions in terms of the values the variables take, how often they take them, the presence of missing data, etc.? (2 points)

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Digital transformation and ERP (Enterprise Resource Planning) strategies have become pivotal for businesses aiming to stay competitive in today’s fast-evolving landscape. At the core of these strategies lies data management — a critical aspect that ensures accurate, reliable, and actionable insights for decision-making.

Watch this comprehensive video on starting data management in your ERP strategy for an in-depth understanding.

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Informed Decision-Making: Reliable data leads to better analytics, which fuels strategic decisions.

Cost Optimization: Effective data management reduces redundancies, eliminates errors, and cuts costs in operations.

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2. Define Your Data Governance Framework

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Real-time data updates

4. Involve Key Stakeholders

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5. Provide Continuous Training

Educate your employees about the importance of clean data and how their inputs impact larger systems like ERP. A data-driven culture begins with informed employees.

How ERP Systems and Data Management Work Hand-in-Hand

High-Impact Benefits of Starting Data Management Now

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Conclusion

Starting your data management journey may seem daunting, but with the right approach and resources, it can transform your digital transformation and ERP strategy into a powerful business enabler.

To gain deeper insights and actionable advice on this topic, make sure to watch this detailed video here.

Take the first step today — because the future of your business depends on how well you manage your data today!

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