diff --git a/blogContent/headerImages/restingHeartRate.svg b/blogContent/headerImages/restingHeartRate.svg
new file mode 100644
index 0000000..f7f6d61
--- /dev/null
+++ b/blogContent/headerImages/restingHeartRate.svg
@@ -0,0 +1,1124 @@
+
+
+
+
diff --git a/blogContent/posts/data-science/graphing-my-life-with-matplotlib.md b/blogContent/posts/data-science/graphing-my-life-with-matplotlib.md
new file mode 100644
index 0000000..73b8526
--- /dev/null
+++ b/blogContent/posts/data-science/graphing-my-life-with-matplotlib.md
@@ -0,0 +1,741 @@
+Let's do a deep dive and start visualizing my life using Fitbit and
+Matplotlib.
+
+# What is Fitbit
+
+[Fitbit](https://www.fitbit.com) is a fitness watch that tracks your sleep, heart rate, and activity.
+Fitbit is able to track your steps, however, it is also able to detect multiple types of activity
+like running, walking, "sport" and biking.
+
+# What is Matplotlib
+
+[Matplotlib](https://matplotlib.org/) is a python visualization library that enables you to create bar graphs, line graphs, distributions and many more things.
+Being able to visualize your results is essential to any person working with data at any scale.
+Although I like [GGplot](https://ggplot2.tidyverse.org/) in R more than Matplotlib, Matplotlib is still my go to graphing library for Python.
+
+# Getting Your Fitbit Data
+
+There are two main ways that you can get your Fitbit data:
+
+- Fitbit API
+- Data Archival Export
+
+
+Since connecting to the API and setting up all the web hooks can be a
+pain, I'm just going to use the data export option because this is
+only for one person. You can export your data here:
+[https://www.fitbit.com/settings/data/export](https://www.fitbit.com/settings/data/export).
+
+
+
+The Fitbit data archive was very organized and kept meticulous records
+of everything. All of the data was organized in separate JSON files
+labeled by date. Fitbit keeps around 1MB of data on you per day; most
+of this data is from the heart rate sensors. Although 1MB of data may
+sound like a ton of data, it is probably a lot less if you store it in
+formats other than JSON. When I downloaded the compressed file it was
+20MB, but when I extracted it, it was 380MB! I've only been using
+Fitbit for 11 months at this point.
+
+
+
+## Sleep
+
+Sleep is something fun to visualize. No matter how much of it you get
+you still feel tired as a college student. In the "sleep_score" folder
+of the exported data you will find a single CSV file with your resting
+heart rate and Fitbit's computed sleep scores. Interesting enough,
+this is the only file that comes in the CSV format, everything else is
+JSON file.
+
+We can read in all the data using a single liner with the
+[Pandas](https://pandas.pydata.org/) python library.
+
+
+
+```python
+import matplotlib.pyplot as plt
+import pandas as pd
+
+sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv')
+```
+
+
+```python
+print(sleep_score_df)
+```
+
+ sleep_log_entry_id timestamp overall_score \
+ 0 26093459526 2020-02-27T06:04:30Z 80
+ 1 26081303207 2020-02-26T06:13:30Z 83
+ 2 26062481322 2020-02-25T06:00:30Z 82
+ 3 26045941555 2020-02-24T05:49:30Z 79
+ 4 26034268762 2020-02-23T08:35:30Z 75
+ .. ... ... ...
+ 176 23696231032 2019-09-02T07:38:30Z 79
+ 177 23684345925 2019-09-01T07:15:30Z 84
+ 178 23673204871 2019-08-31T07:11:00Z 74
+ 179 23661278483 2019-08-30T06:34:00Z 73
+ 180 23646265400 2019-08-29T05:55:00Z 80
+
+ composition_score revitalization_score duration_score \
+ 0 20 19 41
+ 1 22 21 40
+ 2 22 21 39
+ 3 17 20 42
+ 4 20 16 39
+ .. ... ... ...
+ 176 20 20 39
+ 177 22 21 41
+ 178 18 21 35
+ 179 17 19 37
+ 180 21 21 38
+
+ deep_sleep_in_minutes resting_heart_rate restlessness
+ 0 65 60 0.117330
+ 1 85 60 0.113188
+ 2 95 60 0.120635
+ 3 52 61 0.111224
+ 4 43 59 0.154774
+ .. ... ... ...
+ 176 88 56 0.170923
+ 177 95 56 0.133268
+ 178 73 56 0.102703
+ 179 50 55 0.121086
+ 180 61 57 0.112961
+
+ [181 rows x 9 columns]
+
+
+With the Pandas library you can generate Matplotlib graphs. Although
+you can directly use Matplotlib, the wrapper functions using Pandas
+makes it easier to use.
+
+## Sleep Score Histogram
+
+
+```python
+sleep_score_df.hist(column='overall_score')
+```
+
+
+
+
+ array([[]],
+ dtype=object)
+
+
+
+
+
+
+
+## Heart Rate
+
+Fitbit keeps their calculated heart rates in the sleep scores file
+rather than heart. Knowing your resting heart rate is useful because
+it is a good indicator of your overall health.
+
+
+
+
+```python
+sleep_score_df.hist(column='resting_heart_rate')
+```
+
+
+
+
+ array([[]],
+ dtype=object)
+
+
+
+
+
+
+
+## Resting Heart Rate Time Graph
+
+Using the pandas wrapper we can quickly create a heart rate graph over
+time.
+
+
+```python
+sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate(BPM)")
+```
+
+
+
+
+
+
+
+
+
+
+
+
+However, as we notice with the graph above, the time axis is wack. In
+the pandas data frame everything was stored as a string timestamp. We
+can convert this into a datetime object by telling pandas to parse the
+date as it reads it.
+
+
+```python
+sleep_score_df = pd.read_csv('data/sleep/sleep_score.csv', parse_dates=[1])
+sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate(BPM)")
+```
+
+
+
+
+
+
+
+
+
+
+
+
+To fully manipulate the graphs, we need to use some matplotlib code to
+do things like setting the axis labels or make multiple plots right
+next to each other. We can create grab the current axis being used by
+matplotlib by using plt.gca().
+
+
+```python
+ax = plt.gca()
+sleep_score_df.plot(kind='line', y='resting_heart_rate', x ='timestamp', legend=False, title="Resting Heart Rate Graph", ax=ax, figsize=(10, 5))
+plt.xlabel("Date")
+plt.ylabel("Resting Heart Rate (BPM)")
+plt.show()
+
+#plt.savefig('restingHeartRate.svg')
+```
+
+
+
+
+
+The same thing can be done with sleep scores. It is interesting to
+note that the sleep scores rarely vary anything between 75 and 85.
+
+
+```python
+ax = plt.gca()
+sleep_score_df.plot(kind='line', y='overall_score', x ='timestamp', legend=False, title="Sleep Score Time Series Graph", ax=ax)
+plt.xlabel("Date")
+plt.ylabel("Fitbit's Sleep Score")
+plt.show()
+```
+
+
+
+
+
+Using Pandas we can generate a new column with a specific date
+attribute like year, day, month, or weekday. If we add a new column
+for weekday, we can then group by weekday and collapse them all into a
+single column by summing or averaging the value.
+
+
+```python
+temp = pd.DatetimeIndex(sleep_score_df['timestamp'])
+sleep_score_df['weekday'] = temp.weekday
+
+print(sleep_score_df)
+```
+
+ sleep_log_entry_id timestamp overall_score \
+ 0 26093459526 2020-02-27 06:04:30+00:00 80
+ 1 26081303207 2020-02-26 06:13:30+00:00 83
+ 2 26062481322 2020-02-25 06:00:30+00:00 82
+ 3 26045941555 2020-02-24 05:49:30+00:00 79
+ 4 26034268762 2020-02-23 08:35:30+00:00 75
+ .. ... ... ...
+ 176 23696231032 2019-09-02 07:38:30+00:00 79
+ 177 23684345925 2019-09-01 07:15:30+00:00 84
+ 178 23673204871 2019-08-31 07:11:00+00:00 74
+ 179 23661278483 2019-08-30 06:34:00+00:00 73
+ 180 23646265400 2019-08-29 05:55:00+00:00 80
+
+ composition_score revitalization_score duration_score \
+ 0 20 19 41
+ 1 22 21 40
+ 2 22 21 39
+ 3 17 20 42
+ 4 20 16 39
+ .. ... ... ...
+ 176 20 20 39
+ 177 22 21 41
+ 178 18 21 35
+ 179 17 19 37
+ 180 21 21 38
+
+ deep_sleep_in_minutes resting_heart_rate restlessness weekday
+ 0 65 60 0.117330 3
+ 1 85 60 0.113188 2
+ 2 95 60 0.120635 1
+ 3 52 61 0.111224 0
+ 4 43 59 0.154774 6
+ .. ... ... ... ...
+ 176 88 56 0.170923 0
+ 177 95 56 0.133268 6
+ 178 73 56 0.102703 5
+ 179 50 55 0.121086 4
+ 180 61 57 0.112961 3
+
+ [181 rows x 10 columns]
+
+
+
+```python
+print(sleep_score_df.groupby('weekday').mean())
+```
+
+ sleep_log_entry_id overall_score composition_score \
+ weekday
+ 0 2.483733e+10 79.576923 20.269231
+ 1 2.485200e+10 77.423077 20.423077
+ 2 2.490383e+10 80.880000 21.120000
+ 3 2.483418e+10 76.814815 20.370370
+ 4 2.480085e+10 79.769231 20.961538
+ 5 2.477002e+10 78.840000 20.520000
+ 6 2.482581e+10 77.230769 20.269231
+
+ revitalization_score duration_score deep_sleep_in_minutes \
+ weekday
+ 0 19.153846 40.153846 88.000000
+ 1 19.000000 38.000000 83.846154
+ 2 19.400000 40.360000 93.760000
+ 3 19.037037 37.407407 82.592593
+ 4 19.346154 39.461538 94.461538
+ 5 19.080000 39.240000 93.720000
+ 6 18.269231 38.692308 89.423077
+
+ resting_heart_rate restlessness
+ weekday
+ 0 58.576923 0.139440
+ 1 58.538462 0.142984
+ 2 58.560000 0.138661
+ 3 58.333333 0.135819
+ 4 58.269231 0.129791
+ 5 58.080000 0.138315
+ 6 58.153846 0.147171
+
+
+## Sleep Score Based on Day
+
+
+```python
+ax = plt.gca()
+sleep_score_df.groupby('weekday').mean().plot(kind='line', y='overall_score', ax = ax)
+plt.ylabel("Sleep Score")
+plt.title("Sleep Scores on Varying Days of Week")
+plt.show()
+```
+
+
+
+
+
+## Sleep Score Based on Days of Week
+
+
+```python
+ax = plt.gca()
+sleep_score_df.groupby('weekday').mean().plot(kind='line', y='resting_heart_rate', ax = ax)
+plt.ylabel("Resting heart rate (BPM)")
+plt.title("Resting Heart Rate Varying Days of Week")
+plt.show()
+```
+
+
+
+
+
+# Calories
+
+Fitbit keeps all of their calorie data in JSON files representing
+sequence data at 1 minute increments. To extrapolate calorie data we
+need to group by day and then sum the days to get the total calories
+burned per day.
+
+
+```python
+calories_df = pd.read_json("data/calories/calories-2019-07-01.json", convert_dates=True)
+```
+
+
+```python
+print(calories_df)
+```
+
+ dateTime value
+ 0 2019-07-01 00:00:00 1.07
+ 1 2019-07-01 00:01:00 1.07
+ 2 2019-07-01 00:02:00 1.07
+ 3 2019-07-01 00:03:00 1.07
+ 4 2019-07-01 00:04:00 1.07
+ ... ... ...
+ 43195 2019-07-30 23:55:00 1.07
+ 43196 2019-07-30 23:56:00 1.07
+ 43197 2019-07-30 23:57:00 1.07
+ 43198 2019-07-30 23:58:00 1.07
+ 43199 2019-07-30 23:59:00 1.07
+
+ [43200 rows x 2 columns]
+
+
+
+```python
+import datetime
+calories_df['date_minus_time'] = calories_df["dateTime"].apply( lambda calories_df :
+ datetime.datetime(year=calories_df.year, month=calories_df.month, day=calories_df.day))
+
+calories_df.set_index(calories_df["date_minus_time"],inplace=True)
+
+print(calories_df)
+```
+
+ dateTime value date_minus_time
+ date_minus_time
+ 2019-07-01 2019-07-01 00:00:00 1.07 2019-07-01
+ 2019-07-01 2019-07-01 00:01:00 1.07 2019-07-01
+ 2019-07-01 2019-07-01 00:02:00 1.07 2019-07-01
+ 2019-07-01 2019-07-01 00:03:00 1.07 2019-07-01
+ 2019-07-01 2019-07-01 00:04:00 1.07 2019-07-01
+ ... ... ... ...
+ 2019-07-30 2019-07-30 23:55:00 1.07 2019-07-30
+ 2019-07-30 2019-07-30 23:56:00 1.07 2019-07-30
+ 2019-07-30 2019-07-30 23:57:00 1.07 2019-07-30
+ 2019-07-30 2019-07-30 23:58:00 1.07 2019-07-30
+ 2019-07-30 2019-07-30 23:59:00 1.07 2019-07-30
+
+ [43200 rows x 3 columns]
+
+
+
+```python
+calories_per_day = calories_df.resample('D').sum()
+print(calories_per_day)
+```
+
+ value
+ date_minus_time
+ 2019-07-01 3422.68
+ 2019-07-02 2705.85
+ 2019-07-03 2871.73
+ 2019-07-04 4089.93
+ 2019-07-05 3917.91
+ 2019-07-06 2762.55
+ 2019-07-07 2929.58
+ 2019-07-08 2698.99
+ 2019-07-09 2833.27
+ 2019-07-10 2529.21
+ 2019-07-11 2634.25
+ 2019-07-12 2953.91
+ 2019-07-13 4247.45
+ 2019-07-14 2998.35
+ 2019-07-15 2846.18
+ 2019-07-16 3084.39
+ 2019-07-17 2331.06
+ 2019-07-18 2849.20
+ 2019-07-19 2071.63
+ 2019-07-20 2746.25
+ 2019-07-21 2562.11
+ 2019-07-22 1892.99
+ 2019-07-23 2372.89
+ 2019-07-24 2320.42
+ 2019-07-25 2140.87
+ 2019-07-26 2430.38
+ 2019-07-27 3769.04
+ 2019-07-28 2036.24
+ 2019-07-29 2814.87
+ 2019-07-30 2077.82
+
+
+
+```python
+ax = plt.gca()
+calories_per_day.plot(kind='hist', title="Calorie Distribution", legend=False, ax=ax)
+plt.show()
+```
+
+
+
+
+
+
+```python
+ax = plt.gca()
+calories_per_day.plot(kind='line', y='value', legend=False, title="Calories Per Day", ax=ax)
+plt.xlabel("Date")
+plt.ylabel("Calories")
+plt.show()
+```
+
+
+
+
+
+## Calories Per Day Box Plot
+
+Using this data we can turn this into a boxplot to make it easier to
+visualize the distribution of calories burned during the month of
+July.
+
+
+```python
+ax = plt.gca()
+ax.set_title('Calorie Distribution for July')
+ax.boxplot(calories_per_day['value'], vert=False,manage_ticks=False, notch=True)
+plt.xlabel("Calories Burned")
+ax.set_yticks([])
+plt.show()
+```
+
+
+
+
+
+# Steps
+
+Fitbit is known for taking the amount of steps someone takes per day.
+Similar to calories burned, steps taken is stored in time series data
+at 1 minute increments. Since we are interested at the day level data,
+we need to first remove the time component of the dataframe so that we
+can group all the data by date. Once we have everything grouped by
+date, we can sum and produce steps per day.
+
+
+```python
+steps_df = pd.read_json("data/steps-2019-07-01.json", convert_dates=True)
+
+steps_df['date_minus_time'] = steps_df["dateTime"].apply( lambda steps_df :
+ datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day))
+
+steps_df.set_index(steps_df["date_minus_time"],inplace=True)
+print(steps_df)
+```
+
+ dateTime value date_minus_time
+ date_minus_time
+ 2019-07-01 2019-07-01 04:00:00 0 2019-07-01
+ 2019-07-01 2019-07-01 04:01:00 0 2019-07-01
+ 2019-07-01 2019-07-01 04:02:00 0 2019-07-01
+ 2019-07-01 2019-07-01 04:03:00 0 2019-07-01
+ 2019-07-01 2019-07-01 04:04:00 0 2019-07-01
+ ... ... ... ...
+ 2019-07-31 2019-07-31 03:55:00 0 2019-07-31
+ 2019-07-31 2019-07-31 03:56:00 0 2019-07-31
+ 2019-07-31 2019-07-31 03:57:00 0 2019-07-31
+ 2019-07-31 2019-07-31 03:58:00 0 2019-07-31
+ 2019-07-31 2019-07-31 03:59:00 0 2019-07-31
+
+ [41116 rows x 3 columns]
+
+
+
+```python
+steps_per_day = steps_df.resample('D').sum()
+print(steps_per_day)
+```
+
+ value
+ date_minus_time
+ 2019-07-01 11285
+ 2019-07-02 4957
+ 2019-07-03 13119
+ 2019-07-04 16034
+ 2019-07-05 11634
+ 2019-07-06 6860
+ 2019-07-07 3758
+ 2019-07-08 9130
+ 2019-07-09 10960
+ 2019-07-10 7012
+ 2019-07-11 5420
+ 2019-07-12 4051
+ 2019-07-13 15980
+ 2019-07-14 23109
+ 2019-07-15 11247
+ 2019-07-16 10170
+ 2019-07-17 4905
+ 2019-07-18 10769
+ 2019-07-19 4504
+ 2019-07-20 5032
+ 2019-07-21 8953
+ 2019-07-22 2200
+ 2019-07-23 9392
+ 2019-07-24 5666
+ 2019-07-25 5016
+ 2019-07-26 5879
+ 2019-07-27 19492
+ 2019-07-28 4987
+ 2019-07-29 9943
+ 2019-07-30 3897
+ 2019-07-31 166
+
+
+## Steps Per Day Histogram
+
+After the data is in the form that we want, graphing the data is
+straight forward. Two added things I like to do for normal box plots
+is to set the displays to horizontal add the notches.
+
+
+```python
+ax = plt.gca()
+ax.set_title('Steps Distribution for July')
+ax.boxplot(steps_per_day['value'], vert=False,manage_ticks=False, notch=True)
+plt.xlabel("Steps Per Day")
+ax.set_yticks([])
+plt.show()
+```
+
+
+
+
+
+Wrapping that all into a single function we get something like this:
+
+
+```python
+def readFileIntoDataFrame(fName):
+ steps_df = pd.read_json(fName, convert_dates=True)
+
+ steps_df['date_minus_time'] = steps_df["dateTime"].apply( lambda steps_df :
+ datetime.datetime(year=steps_df.year, month=steps_df.month, day=steps_df.day))
+
+ steps_df.set_index(steps_df["date_minus_time"],inplace=True)
+ return steps_df.resample('D').sum()
+
+def graphBoxAndWhiskers(data, title, xlab):
+ ax = plt.gca()
+ ax.set_title(title)
+ ax.boxplot(data['value'], vert=False, manage_ticks=False, notch=True)
+ plt.xlabel(xlab)
+ ax.set_yticks([])
+ plt.show()
+```
+
+
+```python
+graphBoxAndWhiskers(readFileIntoDataFrame("data/steps-2020-01-27.json"), "Steps In January", "Steps Per Day")
+```
+
+
+
+
+
+That is cool, but, what if we could view the distribution for each
+month in the same graph? Based on the two previous graphs, my step
+distribution during July looked distinctly different from my step
+distribution in January. The first difficultly would be to read in
+all the files since Fitbit creates a new file for every month. The
+next thing would be to group them by month and then graph it.
+
+
+```python
+import os
+files = os.listdir("data")
+print(files)
+```
+
+ ['steps-2019-04-02.json', 'steps-2019-08-30.json', 'steps-2020-02-26.json', 'steps-2019-10-29.json', 'steps-2019-07-01.json', 'steps-2020-01-27.json', 'steps-2019-07-31.json', 'steps-2019-06-01.json', 'steps-2019-09-29.json', '.ipynb_checkpoints', 'steps-2019-12-28.json', 'steps-2019-05-02.json', 'calories', 'steps-2019-11-28.json', 'sleep']
+
+
+
+```python
+dfs = []
+for file in files: # this can take 15 seconds
+ if "steps" in file: # finds the steps files
+ dfs.append(readFileIntoDataFrame("data/" + file))
+```
+
+
+```python
+stepsPerDay = pd.concat(dfs)
+graphBoxAndWhiskers(stepsPerDay, "Steps Per Day Last 11 Months", "Steps per Day")
+```
+
+
+
+
+
+
+```python
+print(type(stepsPerDay['value'].to_numpy()))
+print(stepsPerDay['value'].keys())
+
+stepsPerDay['month'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).month
+stepsPerDay['week_day'] = pd.DatetimeIndex(stepsPerDay['value'].keys()).weekday
+
+print(stepsPerDay)
+```
+
+
+ DatetimeIndex(['2019-04-03', '2019-04-04', '2019-04-05', '2019-04-06',
+ '2019-04-07', '2019-04-08', '2019-04-09', '2019-04-10',
+ '2019-04-11', '2019-04-12',
+ ...
+ '2019-12-19', '2019-12-20', '2019-12-21', '2019-12-22',
+ '2019-12-23', '2019-12-24', '2019-12-25', '2019-12-26',
+ '2019-12-27', '2019-12-28'],
+ dtype='datetime64[ns]', name='date_minus_time', length=342, freq=None)
+ value month week_day
+ date_minus_time
+ 2019-04-03 510 4 2
+ 2019-04-04 11453 4 3
+ 2019-04-05 12684 4 4
+ 2019-04-06 12910 4 5
+ 2019-04-07 3368 4 6
+ ... ... ... ...
+ 2019-12-24 5779 12 1
+ 2019-12-25 4264 12 2
+ 2019-12-26 4843 12 3
+ 2019-12-27 9609 12 4
+ 2019-12-28 2218 12 5
+
+ [342 rows x 3 columns]
+
+
+## Graphing Steps by Month
+
+Now that we have columns for the total amount of steps per day and the
+months, we can plot all the data on a single plot using the group by
+operator in the plotting library.
+
+
+```python
+ax = plt.gca()
+ax.set_title('Steps Distribution for July\n')
+stepsPerDay.boxplot(column=['value'], by='month',ax=ax, notch=True)
+plt.xlabel("Month")
+plt.ylabel("Steps Per Day")
+plt.show()
+```
+
+
+
+
+
+
+```python
+ax = plt.gca()
+ax.set_title('Steps Distribution By Week Day\n')
+stepsPerDay.boxplot(column=['value'], by='week_day',ax=ax, notch=True)
+plt.xlabel("Week Day")
+plt.ylabel("Steps Per Day")
+plt.show()
+```
+
+
+
+
+
+## Future Work
+
+Moving forward with this I would like to do more visualizations with
+sleep data and heart rate.
diff --git a/blogContent/posts/data-science/media/vis_my_life/compression.png b/blogContent/posts/data-science/media/vis_my_life/compression.png
new file mode 100644
index 0000000..c9061c3
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/compression.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/dataExport.png b/blogContent/posts/data-science/media/vis_my_life/dataExport.png
new file mode 100644
index 0000000..56871e4
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/dataExport.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_11_1.png b/blogContent/posts/data-science/media/vis_my_life/output_11_1.png
new file mode 100644
index 0000000..9955d1b
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_11_1.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_13_1.png b/blogContent/posts/data-science/media/vis_my_life/output_13_1.png
new file mode 100644
index 0000000..ec00be1
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_13_1.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_15_0.png b/blogContent/posts/data-science/media/vis_my_life/output_15_0.png
new file mode 100644
index 0000000..618fde6
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_15_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_17_0.png b/blogContent/posts/data-science/media/vis_my_life/output_17_0.png
new file mode 100644
index 0000000..e892b5f
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_17_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_22_0.png b/blogContent/posts/data-science/media/vis_my_life/output_22_0.png
new file mode 100644
index 0000000..2c62367
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_22_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_24_0.png b/blogContent/posts/data-science/media/vis_my_life/output_24_0.png
new file mode 100644
index 0000000..11bc90a
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_24_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_30_0.png b/blogContent/posts/data-science/media/vis_my_life/output_30_0.png
new file mode 100644
index 0000000..a411e83
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_30_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_31_0.png b/blogContent/posts/data-science/media/vis_my_life/output_31_0.png
new file mode 100644
index 0000000..8b81b8b
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_31_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_33_0.png b/blogContent/posts/data-science/media/vis_my_life/output_33_0.png
new file mode 100644
index 0000000..4195063
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_33_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_38_0.png b/blogContent/posts/data-science/media/vis_my_life/output_38_0.png
new file mode 100644
index 0000000..2b239af
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_38_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_41_0.png b/blogContent/posts/data-science/media/vis_my_life/output_41_0.png
new file mode 100644
index 0000000..fe7c10a
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_41_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_45_0.png b/blogContent/posts/data-science/media/vis_my_life/output_45_0.png
new file mode 100644
index 0000000..ecf5205
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_45_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_48_0.png b/blogContent/posts/data-science/media/vis_my_life/output_48_0.png
new file mode 100644
index 0000000..b1ca0b5
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_48_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_49_0.png b/blogContent/posts/data-science/media/vis_my_life/output_49_0.png
new file mode 100644
index 0000000..44bcbbf
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_49_0.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_7_1.png b/blogContent/posts/data-science/media/vis_my_life/output_7_1.png
new file mode 100644
index 0000000..621186b
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_7_1.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/output_9_1.png b/blogContent/posts/data-science/media/vis_my_life/output_9_1.png
new file mode 100644
index 0000000..106d86a
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/output_9_1.png differ
diff --git a/blogContent/posts/data-science/media/vis_my_life/restingHeartRate.jpg b/blogContent/posts/data-science/media/vis_my_life/restingHeartRate.jpg
new file mode 100644
index 0000000..f26d8b3
Binary files /dev/null and b/blogContent/posts/data-science/media/vis_my_life/restingHeartRate.jpg differ