Divya shortdiv

## atad.csv

          
            date
            close

            
              26-Mar-12
              606.98

            
              27-Mar-12
              614.48

            
              28-Mar-12
              617.62

            
              29-Mar-12
              609.86

            
              30-Mar-12
              599.55

            
              2-Apr-12
              618.63

            
              3-Apr-12
              629.32

            
              4-Apr-12
              624.31

            
              5-Apr-12
              633.68

## .py
#Forcing the labels to be empty strings and then setting the labels in the legend is one way to prevent the pie charts from repeating labels
labels = 'Female', 'Male'
miles_by_gender_pie.plot.pie(subplots=True, labels=['', ''], figsize=(12,3.8), autopct='%.2f')
plt.axis('equal')
plt.legend(labels=labels)
plt.show()

#Also increasing the size of tick marks was just a matter of doing `plt.tick_params(labelsize=15)`

#This is my visualization of the breakdown of user types by year. It feels like a round about way to do things, but I wasn't sure how to grab just the user types and sum them another way.

## PandasChart.py
#Forcing the labels to be empty strings and then setting the labels in the legend is one way to prevent the pie charts from repeating labels
labels = 'Female', 'Male'
miles_by_gender_pie.plot.pie(subplots=True, labels=['', ''], figsize=(12,3.8), autopct='%.2f')
plt.axis('equal')
plt.legend(labels=labels)
plt.show()

#Also increasing the size of tick marks was just a matter of doing `plt.tick_params(labelsize=15)`

#This is my visualization of the breakdown of user types by year. It feels like a round about way to do things, but I wasn't sure how to grab just the user types and sum them another way.

## index.html
<congress-members></congress-members>

## gist:b4f32982f15b5e8729df3c458a024598
Introduction Q+A
Q: Who am I?
A: I'm Divya, I work as a web developer at the Knight Lab at Northwestern University, where I work on building tools and applications to help journalists tell better stories.

Q: Why ChiPy?
A: I've dabbled in python but wanted a reason to pursue it actively.

Q: Why Data Science?
A: Data Analysis has a direct application to journalism (data journalism is a thing now). By learning about and working on a data project, I hope to build skills and knowledge that I can then convey to students at Medill. I also love the application of data analysis to civic hacking. I first saw this at Chi Hack Night, and I knew I wanted an excuse to learn about data analyses.

## mountainscene.md

      
              1 file
            
          
              0 forks
            
          
              22 comments
            
          
              0 stars
            
          
                shortdiv
                / mountainscene.md
            
            
              Last active
              April 7, 2017 18:43
            
          
    D3 for MSIA

Paste your pens in the comments below


## numberofflights.csv

          
            time
            number of flights

            
              0:00
              8922

            
              1:00
              4659

            
              2:00
              1220

            
              3:00
              668

            
              4:00
              396

            
              5:00
              56171

            
              6:00
              160897

            
              7:00
              166513

            
              8:00
              157501

## sherlock.csv
name	word	count
holmes	holmes	457
little	little	269
time	time	150
door	door	140
matter	matter	125
heard	heard	113
hand	hand	112
house	house	112
found	found	107

## sherlock.csv
letter	frequency
A	.08167
B	.01492
C	.02780
D	.04253
E	.12702
F	.02288
G	.02022
H	.06094
I	.06973

## sherlock.csv
name	word	count
holmes holmes	457
little little	269
time time	150
door door	140
matter matter	125
heard	heard	113
hand hand	112
house house	112
found found	107
	date	close
	26-Mar-12	606.98
	27-Mar-12	614.48
	28-Mar-12	617.62
	29-Mar-12	609.86
	30-Mar-12	599.55
	2-Apr-12	618.63
	3-Apr-12	629.32
	4-Apr-12	624.31
	5-Apr-12	633.68
	#Forcing the labels to be empty strings and then setting the labels in the legend is one way to prevent the pie charts from repeating labels
	labels = 'Female', 'Male'
	miles_by_gender_pie.plot.pie(subplots=True, labels=['', ''], figsize=(12,3.8), autopct='%.2f')
	plt.axis('equal')
	plt.legend(labels=labels)
	plt.show()

	#Also increasing the size of tick marks was just a matter of doing `plt.tick_params(labelsize=15)`

	#This is my visualization of the breakdown of user types by year. It feels like a round about way to do things, but I wasn't sure how to grab just the user types and sum them another way.
	Introduction Q+A
	Q: Who am I?
	A: I'm Divya, I work as a web developer at the Knight Lab at Northwestern University, where I work on building tools and applications to help journalists tell better stories.

	Q: Why ChiPy?
	A: I've dabbled in python but wanted a reason to pursue it actively.

	Q: Why Data Science?
	A: Data Analysis has a direct application to journalism (data journalism is a thing now). By learning about and working on a data project, I hope to build skills and knowledge that I can then convey to students at Medill. I also love the application of data analysis to civic hacking. I first saw this at Chi Hack Night, and I knew I wanted an excuse to learn about data analyses.
	time	number of flights
	0:00	8922
	1:00	4659
	2:00	1220
	3:00	668
	4:00	396
	5:00	56171
	6:00	160897
	7:00	166513
	8:00	157501
	name word count
	holmes holmes 457
	little little 269
	time time 150
	door door 140
	matter matter 125
	heard heard 113
	hand hand 112
	house house 112
	found found 107
	letter frequency
	A .08167
	B .01492
	C .02780
	D .04253
	E .12702
	F .02288
	G .02022
	H .06094
	I .06973