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miruchikio-blog · 7 years ago

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Assignment 4

< final statement >

i suppose that craters size is affected by geological character.

the more wider and deeper, that region's geological feature is more soft and easily erodible than other region.

from given dataset, i get mars craters data and make data management

d/D ration - Diameter graph show that middle area of MARS ( -30' ~ 30' of Latitude ) have high d/D ratio. the results suggest that surface of middle area of MARS may have fine-grained materials.

< main code >

import pandas as pdimport numpy as np

import seaborn

import matplotlib.pyplot as plt

## dataset importdata = pd.read_csv('marscrater_pds.csv', low_memory=False)

## unnecessary column delete.. just trying..

data.drop('MORPHOLOGY_EJECTA_1', axis=1, inplace=True)data.drop('MORPHOLOGY_EJECTA_2', axis=1, inplace=True)data.drop('MORPHOLOGY_EJECTA_3', axis=1, inplace=True)data.drop('NUMBER_LAYERS', axis=1, inplace=True)

# New variable making

data['d_D_ratio'] = data['DEPTH_RIMFLOOR_TOPOG'] / data['DIAM_CIRCLE_IMAGE']

# divide Latitude of mars into 6 group eg. -90~-60, -60~-30, -30~0, 0~30, 30~60, 60~90

def LATI (row):

if (row['LATITUDE_CIRCLE_IMAGE'] >= -90.0) & (row['LATITUDE_CIRCLE_IMAGE'] < -60.0) :

return 1

elif (row['LATITUDE_CIRCLE_IMAGE'] >= -60.0) & (row['LATITUDE_CIRCLE_IMAGE'] < -30.0) :

return 2

elif (row['LATITUDE_CIRCLE_IMAGE'] >= -30.0) & (row['LATITUDE_CIRCLE_IMAGE'] < 0.0) :

return 3

elif (row['LATITUDE_CIRCLE_IMAGE'] >= 0.0) & (row['LATITUDE_CIRCLE_IMAGE'] < 30.0) :

return 4

elif (row['LATITUDE_CIRCLE_IMAGE'] >= 30.0) & (row['LATITUDE_CIRCLE_IMAGE'] < 60.0) :

return 5

elif (row['LATITUDE_CIRCLE_IMAGE'] >= 60.0) & (row['LATITUDE_CIRCLE_IMAGE'] < 90.0) :

return 6

else:

return 0

# subset data to d_D_ratio over 0.001 and creating 'LATI' variable

sub1 = data[(data['d_D_ratio'] > 0.001)]

sub1['LATI'] = sub1.apply (lambda row : LATI (row), axis=1)

#check data set

print("<<< sub1 data set info check >>>")

sub1.info()

print(sub1)

desc1 = sub1['d_D_ratio'].describe()

print (desc1)

desc2 = sub1['DIAM_CIRCLE_IMAGE'].describe()

print (desc2)

desc3 = sub1['DEPTH_RIMFLOOR_TOPOG'].describe()

print (desc3)

desc4 = sub1['LATI'].describe()

print (desc4)

print("<<< Diameter according to LATITUDE >>>")

c11 = sub1.groupby('LATI')['DIAM_CIRCLE_IMAGE'].describe()

print(c11)

print("<<< Depth according to LATITUDE >>>")

c12 = sub1.groupby('LATI')['DEPTH_RIMFLOOR_TOPOG'].describe()

print(c12)

print("<<< d/D ratio according to LATITUDE >>>")

c13 = sub1.groupby('LATI')['d_D_ratio'].describe()

print(c13)

## basic plot Association Between DIAM_CIRCLE_IMAGE and DEPTH_RIMFLOOR_TOPOG

scat1 = seaborn.regplot(x="DIAM_CIRCLE_IMAGE", y="DEPTH_RIMFLOOR_TOPOG", data=sub1)

plt.xlabel('DIAM_CIRCLE_IMAGE')

plt.ylabel('DEPTH_RIMFLOOR_TOPOG')

plt.title('Basic Scatterplot for the Association Between DIAM_CIRCLE_IMAGE and DEPTH_RIMFLOOR_TOPOG')

## bivariate bar graph plot

seaborn.factorplot(x='LATI', y='DIAM_CIRCLE_IMAGE', data=sub1, kind="bar", ci=None)

plt.xlabel('Latitude group')

plt.ylabel('Diameters')

plt.title('Crater Diameter')

seaborn.factorplot(x='LATI', y='DEPTH_RIMFLOOR_TOPOG', data=sub1, kind="bar", ci=None)

plt.xlabel('Latitude group')

plt.ylabel('Depth')

plt.title('Crater Depth')

seaborn.factorplot(x='LATI', y='d_D_ratio', data=sub1, kind="bar", ci=None)

plt.xlabel('Latitude group')

plt.ylabel('d_D_ratio')

plt.title('d/D ratio')

< console output >

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

sub1['LATI'] = sub1.apply (lambda row : LATI (row), axis=1)

<<< sub1 data set info check >>>

Int64Index: 76742 entries, 0 to 374206

Data columns (total 8 columns):

CRATER_ID 76742 non-null object

CRATER_NAME 76742 non-null object

LATITUDE_CIRCLE_IMAGE 76742 non-null float64

LONGITUDE_CIRCLE_IMAGE 76742 non-null float64

DIAM_CIRCLE_IMAGE 76742 non-null float64

DEPTH_RIMFLOOR_TOPOG 76742 non-null float64

d_D_ratio 76742 non-null float64

LATI 76742 non-null int64

dtypes: float64(5), int64(1), object(2)

memory usage: 5.3+ MB

CRATER_ID CRATER_NAME ... d_D_ratio LATI

0 01-000000 ... 0.002680 6

1 01-000001 Korolev ... 0.024019 6

2 01-000002 ... 0.001130 6

3 01-000003 ... 0.001738 6

4 01-000004 ... 0.001496 6

5 01-000005 ... 0.002615 6

6 01-000006 ... 0.001426 6

8 01-000008 ... 0.001884 6

10 01-000010 ... 0.001337 6

11 01-000011 ... 0.002534 6

12 01-000012 Dokka ... 0.034064 6

13 01-000013 ... 0.003214 6

14 01-000014 ... 0.002832 6

15 01-000015 ... 0.021704 6

16 01-000016 ... 0.001858 6

18 01-000018 ... 0.003476 6

20 01-000020 ... 0.040691 6

21 01-000021 ... 0.001829 6

23 01-000023 ... 0.011387 6

24 01-000024 ... 0.001222 6

25 01-000025 ... 0.052645 6

26 01-000026 ... 0.053731 6

27 01-000027 ... 0.052183 6

28 01-000028 Louth ... 0.038864 6

29 01-000029 ... 0.001117 6

30 01-000030 ... 0.003085 6

31 01-000031 ... 0.004045 6

32 01-000032 ... 0.002968 6

33 01-000033 ... 0.003009 6

34 01-000034 ... 0.002124 6

... ... ... ... ...

374163 30-003511 ... 0.023102 1

374168 30-003516 ... 0.006601 1

374169 30-003517 ... 0.016556 1

374170 30-003518 ... 0.009934 1

374173 30-003521 ... 0.039735 1

374175 30-003523 ... 0.016556 1

374176 30-003524 ... 0.019868 1

374178 30-003526 ... 0.023179 1

374179 30-003527 ... 0.016556 1

374183 30-003531 ... 0.016556 1

374184 30-003532 ... 0.006645 1

374185 30-003533 ... 0.023256 1

374186 30-003534 ... 0.039867 1

374187 30-003535 ... 0.026578 1

374188 30-003536 ... 0.009967 1

374190 30-003538 ... 0.046512 1

374191 30-003539 ... 0.026578 1

374192 30-003540 ... 0.013289 1

374193 30-003541 ... 0.013289 1

374194 30-003542 ... 0.013289 1

374196 30-003544 ... 0.009967 1

374197 30-003545 ... 0.013289 1

374199 30-003547 ... 0.029900 1

374200 30-003548 ... 0.009967 1

374201 30-003549 ... 0.006667 1

374202 30-003550 ... 0.013333 1

374203 30-003551 ... 0.020000 1

374204 30-003552 ... 0.053333 1

374205 30-003553 ... 0.013333 1

374206 30-003554 ... 0.013333 1

[76742 rows x 8 columns]

count 76742.000000

mean 0.051168

std 0.039368

min 0.001003

25% 0.018692

50% 0.038339

75% 0.079695

max 0.230769

Name: d_D_ratio, dtype: float64

count 76742.000000

mean 11.022934

std 15.450993

min 1.060000

25% 3.580000

50% 5.870000

75% 12.120000

max 512.750000

Name: DIAM_CIRCLE_IMAGE, dtype: float64

count 76742.000000

mean 0.379789

std 0.360992

min 0.010000

25% 0.120000

50% 0.270000

75% 0.520000

max 4.950000

Name: DEPTH_RIMFLOOR_TOPOG, dtype: float64

count 76742.000000

mean 3.161163

std 1.163746

min 1.000000

25% 2.000000

50% 3.000000

75% 4.000000

max 6.000000

Name: LATI, dtype: float64

<<< Diameter according to LATITUDE >>>

count mean std min 25% 50% 75% max

LATI

1 5067.0 13.575891 16.627828 3.00 4.280 7.30 15.7800 201.29

2 17870.0 12.929284 17.166422 1.11 4.010 7.05 14.8700 427.15

3 25221.0 10.968872 15.588188 1.16 3.480 5.90 12.3300 512.75

4 18715.0 9.506109 14.491434 1.06 3.140 4.92 9.8000 408.23

5 7950.0 9.692323 12.558601 1.14 3.770 5.62 10.2975 220.30

6 1919.0 7.545571 9.244252 1.08 3.425 4.57 7.5850 120.58

<<< Depth according to LATITUDE >>>

count mean std min 25% 50% 75% max

LATI

1 5067.0 0.298145 0.361430 0.01 0.08 0.16 0.36 2.57

2 17870.0 0.346001 0.360894 0.01 0.11 0.22 0.44 3.64

3 25221.0 0.453349 0.364987 0.01 0.19 0.37 0.61 4.95

4 18715.0 0.406766 0.353687 0.01 0.14 0.31 0.57 3.03

5 7950.0 0.270597 0.312241 0.01 0.07 0.15 0.35 3.14

6 1919.0 0.132460 0.249356 0.01 0.02 0.05 0.12 2.36

<<< d/D ratio according to LATITUDE >>>

count mean std ... 50% 75% max

LATI ...

1 5067.0 0.023837 0.015140 ... 0.020942 0.031774 0.125940

2 17870.0 0.036521 0.027535 ... 0.028986 0.049502 0.176849

3 25221.0 0.065411 0.041892 ... 0.065287 0.100806 0.207944

4 18715.0 0.063493 0.040681 ... 0.059524 0.092308 0.230769

5 7950.0 0.035955 0.032962 ... 0.024933 0.048222 0.188811

6 1919.0 0.015370 0.015885 ... 0.009772 0.018972 0.152381

< variables ( Diameter and Depth ) relationship >

< 2nd Variable - crater according to MARS Latitude >

0 notes

miruchikio-blog · 7 years ago

Text

Assignment_3

< code > import pandas as pd import numpy as np

## dataset import data = pd.read_csv('marscrater_pds.csv', low_memory=False) ## check data shape print ("<<< check data shape >>>") print (data.head().to_string())

## check data info print ("<<< check data information >>>") data.info() ## unnecessary column delete just trying.. print ("<<< modify original dataset >>>") data.drop('MORPHOLOGY_EJECTA_1', axis=1, inplace=True) data.drop('MORPHOLOGY_EJECTA_2', axis=1, inplace=True) data.drop('MORPHOLOGY_EJECTA_3', axis=1, inplace=True) data.drop('NUMBER_LAYERS', axis=1, inplace=True) ## check results print ("<<< check modified data information >>>") data.info()

## Null data check.. i don't need this ##print ("<null check>") ##data.isnull().sum()

## variable check c1 = data['LATITUDE_CIRCLE_IMAGE'].value_counts() ##print(c1)

c2 = data['DIAM_CIRCLE_IMAGE'].value_counts() ##print(c2)

c3 = data['DEPTH_RIMFLOOR_TOPOG'].value_counts() ##print(c3) ## new variable making and add col print ("<<< New variable and add column >>>") data['d_D_ratio'] = data['DEPTH_RIMFLOOR_TOPOG'] / data['DIAM_CIRCLE_IMAGE'] c4 = data['d_D_ratio'].value_counts() print(c4)

print ("<<< New variable and frequency distribution >>>") c4 = data['d_D_ratio'].value_counts(sort=True,normalize=True) print(c4)

##group test depth_dia = data.groupby(['d_D_ratio','DEPTH_RIMFLOOR_TOPOG','DIAM_CIRCLE_IMAGE'])['d_D_ratio'].count() print(depth_dia)

c5 = depth_dia.value_counts() print(c5)

< Console Output >

<<< check data shape >>> CRATER_ID CRATER_NAME LATITUDE_CIRCLE_IMAGE LONGITUDE_CIRCLE_IMAGE DIAM_CIRCLE_IMAGE DEPTH_RIMFLOOR_TOPOG MORPHOLOGY_EJECTA_1 MORPHOLOGY_EJECTA_2 MORPHOLOGY_EJECTA_3 NUMBER_LAYERS 0 01-000000 84.367 108.746 82.10 0.22 0 1 01-000001 Korolev 72.760 164.464 82.02 1.97 Rd/MLERS HuBL 3 2 01-000002 69.244 -27.240 79.63 0.09 0 3 01-000003 70.107 160.575 74.81 0.13 0 4 01-000004 77.996 95.617 73.53 0.11 0 <<< check data information >>> <class 'pandas.core.frame.DataFrame'> RangeIndex: 384343 entries, 0 to 384342 Data columns (total 10 columns): CRATER_ID 384343 non-null object CRATER_NAME 384343 non-null object LATITUDE_CIRCLE_IMAGE 384343 non-null float64 LONGITUDE_CIRCLE_IMAGE 384343 non-null float64 DIAM_CIRCLE_IMAGE 384343 non-null float64 DEPTH_RIMFLOOR_TOPOG 384343 non-null float64 MORPHOLOGY_EJECTA_1 384343 non-null object MORPHOLOGY_EJECTA_2 384343 non-null object MORPHOLOGY_EJECTA_3 384343 non-null object NUMBER_LAYERS 384343 non-null int64 dtypes: float64(4), int64(1), object(5) memory usage: 29.3+ MB <<< modify original dataset >>> <<< check modified data information >>> <class 'pandas.core.frame.DataFrame'> RangeIndex: 384343 entries, 0 to 384342 Data columns (total 6 columns): CRATER_ID 384343 non-null object CRATER_NAME 384343 non-null object LATITUDE_CIRCLE_IMAGE 384343 non-null float64 LONGITUDE_CIRCLE_IMAGE 384343 non-null float64 DIAM_CIRCLE_IMAGE 384343 non-null float64 DEPTH_RIMFLOOR_TOPOG 384343 non-null float64 dtypes: float64(4), object(2) memory usage: 17.6+ MB <<< New variable and add column >>> 0.000000 307529 0.062500 73 0.090909 71 0.066667 67 0.125000 66 0.058824 58 0.100000 51 0.045455 50 0.076923 50 0.031250 48 0.111111 46 0.071429 46 0.083333 45 0.055556 45 0.033333 44 0.111111 44 0.035714 44 0.040000 44 0.032258 44 0.015625 41 0.043478 40 0.019608 39 0.080000 38 0.034483 36 0.083333 36 0.047619 36 0.020000 35 0.018182 35 0.022727 34 0.038462 34 0.028612 1 0.083659 1 0.032145 1 0.011746 1 0.139583 1 0.043967 1 0.029418 1 0.058961 1 0.004144 1 0.010217 1 0.041833 1 0.006731 1 0.042339 1 0.007267 1 0.182283 1 0.057671 1 0.089941 1 0.023052 1 0.028659 1 0.035338 1 0.077572 1 0.010238 1 0.138085 1 0.019572 1 0.012155 1 0.134986 1 0.070524 1 0.031710 1 0.127566 1 0.050777 1 Name: d_D_ratio, Length: 38943, dtype: int64 <<< New variable and frequency distribution >>> 0.000000 0.800142 0.062500 0.000190 0.090909 0.000185 0.066667 0.000174 0.125000 0.000172 0.058824 0.000151 0.100000 0.000133 0.045455 0.000130 0.076923 0.000130 0.031250 0.000125 0.111111 0.000120 0.071429 0.000120 0.083333 0.000117 0.055556 0.000117 0.033333 0.000114 0.111111 0.000114 0.035714 0.000114 0.040000 0.000114 0.032258 0.000114 0.015625 0.000107 0.043478 0.000104 0.019608 0.000101 0.080000 0.000099 0.034483 0.000094 0.083333 0.000094 0.047619 0.000094 0.020000 0.000091 0.018182 0.000091 0.022727 0.000088 0.038462 0.000088 0.028612 0.000003 0.083659 0.000003 0.032145 0.000003 0.011746 0.000003 0.139583 0.000003 0.043967 0.000003 0.029418 0.000003 0.058961 0.000003 0.004144 0.000003 0.010217 0.000003 0.041833 0.000003 0.006731 0.000003 0.042339 0.000003 0.007267 0.000003 0.182283 0.000003 0.057671 0.000003 0.089941 0.000003 0.023052 0.000003 0.028659 0.000003 0.035338 0.000003 0.077572 0.000003 0.010238 0.000003 0.138085 0.000003 0.019572 0.000003 0.012155 0.000003 0.134986 0.000003 0.070524 0.000003 0.031710 0.000003 0.127566 0.000003 0.050777 0.000003 Name: d_D_ratio, Length: 38943, dtype: float64 d_D_ratio DEPTH_RIMFLOOR_TOPOG DIAM_CIRCLE_IMAGE -0.016400 -0.42 25.61 1 -0.002582 -0.03 11.62 1 -0.001863 -0.03 16.10 1 -0.001386 -0.02 14.43 1 -0.000973 -0.01 10.28 1 -0.000925 -0.01 10.81 1 -0.000586 -0.02 34.11 1 -0.000548 -0.02 36.51 1 -0.000528 -0.01 18.95 1 -0.000405 -0.02 49.44 1 0.000000 0.00 1.00 3129 1.01 6298 1.02 6077 1.03 6035 1.04 5941 1.05 5771 1.06 5555 1.07 5454 1.08 5417 1.09 5197 1.10 5087 1.11 4883 1.12 4846 1.13 4686 1.14 4455 1.15 4559 1.16 4394 1.17 4248 1.18 4264 1.19 3995

0.194373 0.76 3.91 1 0.194667 0.73 3.75 1 0.194986 0.70 3.59 1 0.195122 0.48 2.46 1 0.195446 1.03 5.27 1 0.196203 0.93 4.74 1 0.197059 0.67 3.40 1 0.198238 0.90 4.54 1 0.198488 1.05 5.29 1 0.198661 0.89 4.48 1 0.198738 0.63 3.17 1 0.200000 1.49 7.45 1 0.200418 0.96 4.79 1 0.201172 1.03 5.12 1 0.201717 1.41 6.99 1 0.202083 0.97 4.80 1 0.202151 0.94 4.65 1 0.203008 0.81 3.99 1 0.203160 0.90 4.43 1 0.204545 0.81 3.96 1 0.205357 0.69 3.36 1 0.205479 0.75 3.65 1 0.205832 1.20 5.83 1 0.207944 0.89 4.28 1 0.208038 0.88 4.23 1 0.208417 1.04 4.99 1 0.213636 0.94 4.40 1 0.222527 0.81 3.64 1 0.230461 1.15 4.99 1 0.230769 0.96 4.16 1 Name: d_D_ratio, Length: 52716, dtype: int64 1 38357 2 7535 3 3089 4 1546 5 777 6 427 7 233 8 125 9 88 10 44 11 35 13 20 12 15 16 11 15 10 24 8 14 8 32 7 50 7 28 6 30 6 44 6 18 6 21 6 29 6 33 5 27 5 23 5 25 5 34 5 1009 1 1077 1 1299 1 565 1 277 1 117 1 85 1 1908 1 1396 1 884 1 372 1 308 1 276 1 84 1 1971 1 435 1 1201 1 2450 1 51 1 1650 1 786 1 338 1 306 1 114 1 6035 1 1745 1 5555 1 1393 1 1233 1 591 1 Name: d_D_ratio, Length: 281, dtype: int64

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miruchikio-blog · 7 years ago

Text

Assignment2

my first python program

1. Python Code

# -*- coding: utf-8 -*- """ Created on Mon Jul 9 20:54:20 2018

@author: jun """

## inport library import pandas import numpy

data = pandas.read_csv('C:/jun/marscrater_pds.csv') ## read dataset ##print (data.shape) ## check for dataset (384343, 10)

crater_id = data["CRATER_ID"] ##print(crater_id)

## make new dataset lati = data["LATITUDE_CIRCLE_IMAGE"] ## latitude longi = data["LONGITUDE_CIRCLE_IMAGE"] ## longitute

print(" <<< diameter of craters >>>") dia = data["DIAM_CIRCLE_IMAGE"] ## diameter print(dia)

print(" <<< depth of craters >>>") depth = data["DEPTH_RIMFLOOR_TOPOG"] ## depth print(depth)

## 5 < diameter < 50 dia1 = data[(data["DIAM_CIRCLE_IMAGE"] >= 5) & (data["DIAM_CIRCLE_IMAGE"] <= 50)] print(dia1)

## copy of dia2 = dia1.copy()

## count for diameter p1 = dia2["DIAM_CIRCLE_IMAGE"].value_counts(sort=False) print(p1)

## percentage for diameter p2 = dia2["DIAM_CIRCLE_IMAGE"].value_counts(sort=False, normalize=True) print(p2)

2. Console Output

<<< diameter of craters >>> 0 82.10 1 82.02 2 79.63 3 74.81 4 73.53 5 72.66 6 70.11 7 63.57 8 58.40 9 55.24 10 52.37 11 51.31 12 51.08 13 49.78 14 49.43 15 48.84 16 48.44 17 47.20 18 46.03 19 45.85 20 45.71 21 43.75 22 43.57 23 43.03 24 40.90 25 39.51 26 36.85 27 36.41 28 36.28 29 35.82 384313 1.00 384314 1.00 384315 1.00 384316 1.00 384317 1.00 384318 1.00 384319 1.00 384320 1.00 384321 1.00 384322 1.00 384323 1.00 384324 1.00 384325 1.00 384326 1.00 384327 1.00 384328 1.00 384329 1.00 384330 1.00 384331 1.00 384332 1.00 384333 1.00 384334 1.00 384335 1.00 384336 1.00 384337 1.00 384338 1.00 384339 1.00 384340 1.00 384341 1.00 384342 1.00 Name: DIAM_CIRCLE_IMAGE, Length: 384343, dtype: float64 <<< depth of craters >>> 0 0.22 1 1.97 2 0.09 3 0.13 4 0.11 5 0.19 6 0.10 7 0.05 8 0.11 9 0.00 10 0.07 11 0.13 12 1.74 13 0.16 14 0.14 15 1.06 16 0.09 17 0.04 18 0.16 19 0.03 20 1.86 21 0.08 22 0.03 23 0.49 24 0.05 25 2.08 26 1.98 27 1.90 28 1.41 29 0.04

384313 0.00 384314 0.00 384315 0.00 384316 0.00 384317 0.00 384318 0.00 384319 0.00 384320 0.00 384321 0.00 384322 0.00 384323 0.00 384324 0.00 384325 0.00 384326 0.00 384327 0.00 384328 0.00 384329 0.00 384330 0.00 384331 0.00 384332 0.00 384333 0.00 384334 0.00 384335 0.00 384336 0.00 384337 0.00 384338 0.00 384339 0.00 384340 0.00 384341 0.00 384342 0.00 Name: DEPTH_RIMFLOOR_TOPOG, Length: 384343, dtype: float64 CRATER_ID ... NUMBER_LAYERS 13 01-000013 ... 0 14 01-000014 ... 0 15 01-000015 ... 0 16 01-000016 ... 0 17 01-000017 ... 0 18 01-000018 ... 0 19 01-000019 ... 0 20 01-000020 ... 2 21 01-000021 ... 0 22 01-000022 ... 0 23 01-000023 ... 0 24 01-000024 ... 0 25 01-000025 ... 1 26 01-000026 ... 4 27 01-000027 ... �� 3 28 01-000028 ... 1 29 01-000029 ... 0 30 01-000030 ... 0 31 01-000031 ... 0 32 01-000032 ... 0 33 01-000033 ... 0 34 01-000034 ... 0 35 01-000035 ... 0 36 01-000036 ... 0 37 01-000037 ... 1 38 01-000038 ... 0 39 01-000039 ... 2 40 01-000040 ... 3 41 01-000041 ... 0 42 01-000042 ... 0 ... ... ... 372782 30-002130 ... 0 372783 30-002131 ... 0 372784 30-002132 ... 0 372785 30-002133 ... 0 372786 30-002134 ... 0 372787 30-002135 ... 1 372788 30-002136 ... 0 372789 30-002137 ... 1 372790 30-002138 ... 0 372791 30-002139 ... 0 372792 30-002140 ... 1 372793 30-002141 ... 1 372794 30-002142 ... 0 372795 30-002143 ... 0 372796 30-002144 ... 0 372797 30-002145 ... 0 372798 30-002146 ... 1 372799 30-002147 ... 1 372800 30-002148 ... 1 372801 30-002149 ... 0 372802 30-002150 ... 1 372803 30-002151 ... 0 372804 30-002152 ... 0 372805 30-002153 ... 0 372806 30-002154 ... 0 372807 30-002155 ... 0 372808 30-002156 ... 0 372809 30-002157 ... 0 372810 30-002158 ... 0 372811 30-002159 ... 1

[45528 rows x 10 columns] 8.00 46 32.00 3 12.00 25 16.00 8 10.00 22 37.32 2 49.34 1 10.66 15 15.87 8 13.32 10 40.43 1 30.16 3 30.13 2 10.64 18 30.43 4 9.16 42 38.90 2 38.18 3 43.44 3 14.76 12 47.21 2 9.43 19 17.38 7 9.32 21 15.45 12 32.96 6 11.51 18 43.18 1 30.95 3 48.40 1 .. 45.87 1 13.91 18 27.93 4 45.78 1 31.58 5 11.74 22 7.98 34 30.51 3 11.22 19 7.78 51 42.56 2 46.40 1 29.91 2 31.79 4 7.99 33 39.24 1 13.89 16 31.65 1 7.46 20 13.61 11 15.80 7 31.98 1 21.89 6 34.48 2 21.61 3 7.95 40 49.66 1 47.63 3 47.62 3 30.12 2 Name: DIAM_CIRCLE_IMAGE, Length: 4139, dtype: int64 8.00 0.001010 32.00 0.000066 12.00 0.000549 16.00 0.000176 10.00 0.000483 37.32 0.000044 49.34 0.000022 10.66 0.000329 15.87 0.000176 13.32 0.000220 40.43 0.000022 30.16 0.000066 30.13 0.000044 10.64 0.000395 30.43 0.000088 9.16 0.000923 38.90 0.000044 38.18 0.000066 43.44 0.000066 14.76 0.000264 47.21 0.000044 9.43 0.000417 17.38 0.000154 9.32 0.000461 15.45 0.000264 32.96 0.000132 11.51 0.000395 43.18 0.000022 30.95 0.000066 48.40 0.000022 45.87 0.000022 13.91 0.000395 27.93 0.000088 45.78 0.000022 31.58 0.000110 11.74 0.000483 7.98 0.000747 30.51 0.000066 11.22 0.000417 7.78 0.001120 42.56 0.000044 46.40 0.000022 29.91 0.000044 31.79 0.000088 7.99 0.000725 39.24 0.000022 13.89 0.000351 31.65 0.000022 7.46 0.000439 13.61 0.000242 15.80 0.000154 31.98 0.000022 21.89 0.000132 34.48 0.000044 21.61 0.000066 7.95 0.000879 49.66 0.000022 47.63 0.000066 47.62 0.000066 30.12 0.000044 Name: DIAM_CIRCLE_IMAGE, Length: 4139, dtype: float64

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miruchikio-blog · 7 years ago

Text

Assignment

1. I choose marscraters and simple question -->

" is crater's location associated with diameters? "

2. my topic is relationship between crater location(Latitude) and diameter size

3. i'm interested in exploring the association between location and crater's size ( diameter and new variable - d/D ratio)

4. according to "Geographical Distribution of Crater Depths on Mars" by Tomasz F.Stepinski, global distribution of d/D ratio provide "observational" support for existence of cryosphere with varing depth.

6. if i get data of crater's size according to latitude, we can guess geological character of mars

< Literature Review >

Introduction

The purpose of this review is to examine the distribution and formation of mars craters for finding second topic.

There are many kind of formation and shape of mars craters. first topic simply is that what associated with diameter and depth of craters.

From literature review, depth-diameter ratio can be indicator of MARS geological charcacter.

Methodology of Review

http://scholar.google.com were employed to search for literature review

The keywords used in searching websites were : Mars Craters, Mars Surface, Astronomical Photometry, Depth, Diameters, Impact Damage, Mars Photographs

Overview of Research Studies

In specific region of mars – Amazonis-Memnonia region, Depth to Diameter ratio for fresh craters display greater than expected.

Average of depth-diameter ratios are approximately 0.23(+-4), 0.25(+-3) for simple crater and complex crater repectively.

However, in Amazonis-Memnonia region, depth-diameter ratios are approximately 0.29(+-5), 0.21(+-4)

The increase in depth-diameter ratio is considered due to geologic character on mars.

Discussion and Second Topic

MARS cater formation can be affected by geologic character.

Therefore, geological material and properties of MARS can be infered by investigating depth-diameter ratios.

Using dataset that has 378,540 craters information, MARS geological character according to latitude could be obtained statistically.

Appendix

References

Barlow, N.G. (1993). Increased depth-diameter ratios in the Medusae Fossae Formation deposits of Mars

In its Twenty-fourth Lunar and Planetary Science Conference. Part 1: A-F p 61-62 (SEE N94-12015 01-91)

Barlow, N.G. (1993). Depth-diameter ratios for Martian impact craters: Implications for target properties and episodes of degradation

In its Mars: Past, Present, and Future. Results from the MSATT Program, Part 1 p 1 (SEE N94-33190 09-91)

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