karthika-perumal

[karthika-perumal]

Cleaned up the primary variable!

My primary variable in question is Ejecta Morphology (MORPHOLOGY_EJECTA_1) and I have cleaned up the primary variable. I have chosen the subsets of the other 3 variables namely Diameter (DIAM_CIRCLE_IMAGE), Depth (DEPTH_RIMFLOOR_TOPOG) and Number of Layers (NUMBER_LAYERS) based on the not null values of the primary variable! I have also grouped the variables for better understanding using bin function. My code: ""

import pandas import numpy

data = pandas.read_csv('MarsCrater.csv', low_memory=False) data.columns = map(str.upper, data.columns)

print("Number of Observations =", len(data)) # rows print("Number of Variables =", len(data.columns)) # columns

data['MORPHOLOGY_EJECTA_1'] = data['MORPHOLOGY_EJECTA_1'].astype(str).str.strip() data['MORPHOLOGY_EJECTA_1'] = data['MORPHOLOGY_EJECTA_1'].replace('', numpy.nan)

data.loc[data['MORPHOLOGY_EJECTA_1'].isnull() & data['DIAM_CIRCLE_IMAGE'].isnull(), 'DIAM_CIRCLE_IMAGE'] = 50000 data.loc[data['MORPHOLOGY_EJECTA_1'].isnull() & data['DEPTH_RIMFLOOR_TOPOG'].isnull(), 'DEPTH_RIMFLOOR_TOPOG'] = 10 data.loc[data['MORPHOLOGY_EJECTA_1'].isnull() & data['NUMBER_LAYERS'].isnull(), 'NUMBER_LAYERS'] = 10

print("Primary Ejecta Morphology") c1 = data['MORPHOLOGY_EJECTA_1'].value_counts(sort=False, dropna=False) print(c1)

print("Primary Ejecta Morphology as %") p1 = data['MORPHOLOGY_EJECTA_1'].value_counts(sort=False, normalize=True, dropna=False) * 100 print(p1)

sub2 = data[data['MORPHOLOGY_EJECTA_1'].notnull()].copy()

dia_bins = [0, 25, 50, data['DIAM_CIRCLE_IMAGE'].max()] dia_names = ['Minor', 'Intermediate', 'Major'] sub2['DIAMETER_NAMES'] = pandas.cut(sub2['DIAM_CIRCLE_IMAGE'], bins=dia_bins, labels=dia_names)

print("Crater Diameter") c2 = sub2['DIAMETER_NAMES'].value_counts(sort=False) print(c2)

print("Crater Diameter as %") p2 = sub2['DIAMETER_NAMES'].value_counts(sort=False, normalize=True) * 100 print(p2)

dep_bins = [0, 0.5, 1, data['DEPTH_RIMFLOOR_TOPOG'].max()] dep_names = ['Low', 'Mid', 'High'] sub2['DEPTH_NAMES'] = pandas.cut(sub2['DEPTH_RIMFLOOR_TOPOG'], bins=dep_bins, labels=dep_names)

print("Crater Depth") c3 = sub2['DEPTH_NAMES'].value_counts(sort=False) print(c3)

print("Crater Depth as %") p3 = sub2['DEPTH_NAMES'].value_counts(sort=False, normalize=True) * 100 print(p3)

print("Number of layers") c4 = sub2['NUMBER_LAYERS'].value_counts(sort=False) print(c4)

print("Number of layers as %") p4 = sub2['NUMBER_LAYERS'].value_counts(sort=False, normalize=True) * 100 print(p4)

""" Output: Number of Observations = 384343 Number of Variables = 10

Primary Ejecta Morphology NaN 339718 Rd/MLERS 199 Rd 24892 Rd/DLEPC/DLERS 33 SLERS 4828

MLEPC/MLERC/MSLEPS 1 DLERCPd/DLERSPd 1 MLERSRd 1 DLERC/Rd/SLERS 1 Rd/SLEPCRd 1 Primary Ejecta Morphology as % NaN 88.389277 Rd/MLERS 0.051777 Rd 6.476507 Rd/DLEPC/DLERS 0.008586 SLERS 1.256170

MLEPC/MLERC/MSLEPS 0.000260 DLERCPd/DLERSPd 0.000260 MLERSRd 0.000260 DLERC/Rd/SLERS 0.000260 Rd/SLEPCRd 0.000260 Crater Diameter Minor 42070 Intermediate 1866 Major 689 Crater Diameter as % Minor 94.274510 Intermediate 4.181513 Major 1.543978 Crater Depth Low 23381 Mid 11582 High 3673 Crater Depth as % Low 60.516099 Mid 29.977223 High 9.506678 Number of layers 3 739 0 24894 2 3435 1 15467 4 85 5 5 Number of layers as % 3 1.656022 0 55.784874 2 7.697479 1 34.659944 4 0.190476 5 0.011204

[karthika-perumal]

First Program for my Research: How Do Crater Size and Depth Influence Ejecta Morphology and the Number of Ejecta Layers in Martian Impact Craters?

I have come up with my first program focusing on frequency distributions of the variables essential for my research. Below you will see the program highlighting the variables such as, 1. Primary Ejecta Morphology: MORPHOLOGY_EJECTA_1

2. Crater Diameter (Grouped accordingly): DIAM_CIRCLE_IMAGE

3. Crater Depth (Grouped accordingly): DEPTH_RIMFLOOR_TOPOG

4. Bonus - Number of layers!: NUMBER_LAYERS

Primary Ejecta Morphology subset is also displayed among the 384343 observations in the dataset. My Code:

"

import pandas

data = pandas.read_csv('MarsCrater.csv', low_memory=False)

data.columns = map(str.upper, data.columns)

print("Number of Observations =", len(data)) # rows print("Number of Variables =",len(data.columns)) # columns

data['MORPHOLOGY_EJECTA_1'] = data['MORPHOLOGY_EJECTA_1'].astype(str).str.strip() not_null = data[data['MORPHOLOGY_EJECTA_1'] != ''] not_null = data[data['MORPHOLOGY_EJECTA_1'] != ''] print("Number of Observations for Primary Ejecta Morphology =", len(not_null)) # subset

print("Primary Ejecta Morphology") c1 = data['MORPHOLOGY_EJECTA_1'].value_counts(sort=False) print(c1)

print("Primary Ejecta Morphology as %") p1 = data['MORPHOLOGY_EJECTA_1'].value_counts(sort=False, normalize=True) * 100 print(p1)

dia_bins = [0, 25, 50, data ['DIAM_CIRCLE_IMAGE'].max()] dia_names = ['Minor', 'Intermediate', 'Major'] data['DIAMETER_NAMES'] = pandas.cut(data['DIAM_CIRCLE_IMAGE'], bins=dia_bins, labels=dia_names)

print("Crater Diameter") c2 = data['DIAMETER_NAMES'].value_counts(sort=False) print(c2)

print("Crater Diameter as %") p2 = data['DIAMETER_NAMES'].value_counts(sort=False, normalize=True) * 100 print(p2)

dep_bins = [0, 0.5, 1, data ['DEPTH_RIMFLOOR_TOPOG'].max()] dep_names = ['Low', 'Mid', 'High'] data['DEPTH_NAMES'] = pandas.cut(data['DEPTH_RIMFLOOR_TOPOG'], bins=dep_bins, labels=dep_names)

print("Crater Depth") c3 = data['DEPTH_NAMES'].value_counts(sort=False) print(c3)

print("Crater Depth as %") p3 = data['DEPTH_NAMES'].value_counts(sort=False, normalize=True) * 100 print(p3)

print("Number of layers") c4 = data['NUMBER_LAYERS'].value_counts(sort=False) print(c4)

print("Number of layers as %") p4 = data['NUMBER_LAYERS'].value_counts(sort=False, normalize=True) * 100 print(p4)

"

Output:

Number of Observations = 384343

Number of Variables = 10

Number of Observations for Primary Ejecta Morphology = 44625

Primary Ejecta Morphology

Rd/MLERS                                      199

Rd                                                      24892

Rd/DLEPC/DLERS                     33

SLERS                                              4828

MLEPC/MLERC/MSLEPS         1

DLERCPd/DLERSPd                   1

MLERSRd                                       1

DLERC/Rd/SLERS                       1

Rd/SLEPCRd                                 1

Primary Ejecta Morphology as %

Rd/MLERS                                      0.051777

Rd                                                      6.476507

Rd/DLEPC/DLERS                      0.008586

SLERS                                              1.256170

MLEPC/MLERC/MSLEPS        0.000260

DLERCPd/DLERSPd                   0.000260

MLERSRd                                       0.000260

DLERC/Rd/SLERS                      0.000260

Rd/SLEPCRd                                 0.000260

Crater Diameter

Minor                  376464

Intermediate  5809

Major                  2070

Crater Diameter as %

Minor                  97.950008

Intermediate  1.511410

Major                  0.538581

Crater Depth

Low      56653

Mid       15343

High     4808

Crater Depth as %

Low      73.763085

Mid       19.976824

High     6.260091

Number of layers

0            364612

3            739

2            3435

1            15467

4            85

5            5

Number of layers as %

0            94.866304

3            0.192276

2            0.893733

1            4.024270

4            0.022116

5            0.001301

[karthika-perumal]

Coursera - Data Analysis and Interpretation Specialization

I have chosen Mars Craters for my research dataset! Research question: How Do Crater Size and Depth Influence Ejecta Morphology in Mars Crater Data?

Topic 2: How Do Crater Size and Depth Influence Ejecta Morphology and the Number of Ejecta Layers in Martian Impact Craters?

Abstract of the study:

Ejecta morphology offers a window into the impact processes and surface properties of planetary bodies. This study leverages a high-resolution Mars crater dataset comprising over 44,000 entries among 380k entries with classified ejecta morphologies, focusing on how crater diameter and depth influence ejecta type. Crater size and rim-to-floor depth are examined whether they serve as reliable predictors of ejecta morphology complexity. Using statistical methods, we assess the relationship between crater dimensions and the occurrence of specific ejecta morphologies and number of layers.

Research Papers Referred:

Nadine G. Barlow., "Martian impact crater ejecta morphologies as indicators of the distribution of subsurface volatiles"

R. H. Hoover1 , S. J. Robbins , N. E. Putzig, J. D. Riggs, and B. M. Hynek. "Insight Into Formation Processes of Layered Ejecta Craters onMars From Thermophysical Observations"