Brad Lucas

A blog mostly about programming
July 1, 2017

Coin Market Cap

There is a useful page called CryptoCurrency Market Capitalizations for viewing the current state of the crytocurency markets.

https://coinmarketcap.com/assets/views/all/

The site shows all currencies runing today on a number of platforms. I'm interested in the ones running on Ethereum which have a Market Cap. Since, the site doesn't have this specific filtering capability I thought it would make a good project to grab the data from the page and filter it the way I'd like.

To do this I decided to investigate Pandas and it's read_html function for pulling data in from html tables.

The following are notes for a Python script that I wrote to pull data from the CryptoCurrency Market Capitalizations, massage the data and show it in useful formats.

Requirements

Setup a virtualenv with the following libraries.

tabulate
pandas
beautifulsoup4
html5lib
lxml
numpy

Read Table

When you investigate the html returned for the page you need to find how the table of data is identified. On inspection you'll see that the table has an id of assets-all. The following shows how you can read this table with Pandas into a DataFrame.

url = 'https://coinmarketcap.com/assets/views/all/'

# Use Pandas to return first table on page
#
df = pd.read_html(url, attrs = {'id': 'assets-all'})[0]

Column Names

The columns have the names of the table columns which I think are a bit unwieldy to use because they have symols and spaces in them. I changed them to sorter single word names.

# Original column names
#
# [ 0,    1,         2,          3,          4,         5,                   6,            7,        8,     9
# ['#', 'Name', 'Platform', 'Market Cap', 'Price', 'Circulating Supply', 'Volume (24h)', '% 1h', '% 24h', '% 7d']

# New column names
#
df.columns = ['#', 'Name', 'Platform', 'MarketCap', 'Price', 'Supply', 'VolumeDay', 'pctHour', 'pctDay', 'pctWeek']

Data Cleanup

Looking at the data you'll see that number fields have $, % and comma characters. These need to be removed so we can sort them numerically. Also, all the columns have an object type and we'll need them to be some sort of numerica for proper behavior.

# Clean the data with 'numbers' by removing $, % and , characters
#
df['Price'] = df['Price'].str.replace('$', '')
df['MarketCap'] = df['MarketCap'].str.replace('$', '')
df['MarketCap'] = df['MarketCap'].str.replace(',', '')
df['VolumeDay'] = df['VolumeDay'].str.replace('$', '')
df['VolumeDay'] = df['VolumeDay'].str.replace(',', '')
df['VolumeDay'] = df['VolumeDay'].str.replace('Low Vol', '0')
df['pctHour'] = df['pctHour'].str.replace('%', '')
df['pctDay'] = df['pctDay'].str.replace('%', '')
df['pctWeek'] = df['pctWeek'].str.replace('%', '')

# Covert 'number' columns to numeric type so they will sort as we'd like
#
def coerce_df_columns_to_numeric(df, column_list):
    df[column_list] = df[column_list].apply(pd.to_numeric, errors='coerce')


coerce_df_columns_to_numeric(df, ['MarketCap', 'Price', 'Supply', 'VolumeDay', 'pctHour', 'pctDay', 'pctWeek'])

To have a column that sorts the name nicely you can create an upper case name.

# Build an upper case name column so we can sort on it more easily
#
df['NameUpper'] = map(lambda x: x.upper(), df['Name'])

And lastly, we only want the Ethereum data with rows which have a MarketCap value.

# Filter so we only have rows which are Ethereum and which have a value for Market Cap
#
df = df.loc[(df['Platform'] == 'Ethereum') & (df['MarketCap'] != '?')]

Report

The following is one report displayed using tabulate. The source code in the repo listed below shows a few other example reports. The following was generated at 2017-07-01 09:07.

Name                MarketCap     Price      Supply    VolumeDay
----------------  -----------  --------  ----------  -----------
Aragon               77232067       2.3    33605167       581475
Arcade Token          2605530       1.2     2164691            0
Augur               289609100     26.33    11000000      4210830
Basic Attenti...    140634000  0.140634  1000000000      1450090
BCAP                 17505300      1.75    10000000       123788
Bitpark Coin          5708738  0.076117    75000000            0
Chronobank           14923518     21.02      710113       541856
Cofound.it           22082125  0.176657   125000000       580521
Creditbit             8750042  0.736853    11874881       359370
DigixDAO            162637000     81.32     2000000       303509
Edgeless             44011846  0.538422    81742288       699753
Ethbits                  1306   0.00307      425388            0
Ethereum Movi...      3605906  0.540886     6666666         3786
Etheroll             28676126       4.1     7001623        25658
FirstBlood          128400869       1.5    85558371      8141070
Gnosis              357368003    323.53     1104590     12010600
Golem               384864116  0.462004   833032000      4593380
Humaniq              26695588  0.163919   162858414       349474
Iconomi             320927340      3.69    87000000      1517730
iDice                 1439145  0.916062     1571013         8511
iExec RLC            43572989  0.551063    79070793       210195
Legends Room          3330340      1.67     2000000       571581
Lunyr                 6812330      2.96     2297853       194785
Matchpool            18527325  0.247031    75000000       206184
MCAP                 98717644      4.84    20383236       265530
Melon                42662775     71.18      599400       318787
Minereum              3193667      5.29      603585        39461
Nexium               19845651  0.298334    66521586      1003330
Numeraire            66873477     54.66     1223451     11890600
Patientory           13120520  0.187436    70000000      1151120
Pluton               11699988     13.76      850000       129772
Quantum              23432939  0.284194    82454023       118966
Quantum Resis...     37007412  0.711681    52000000       546218
RouletteToken         5681468  0.562946    10092385        78663
Round                49461925   0.05819   850000000       304667
SingularDTV         100459800  0.167433   600000000       280495
Status              159849442   0.04606  3470483788     11907400
Swarm City           17365793      2.36     7357576        40417
TaaS                 21002345      2.58     8146001       194969
TokenCard            25058443      1.06    23644056       513609
Unity Ingot          16015247  0.079283   202000000       413868
Veritaseum          161739277     82.21     1967282       370198
VOISE                 1294737      1.57      825578         5121
vSlice               32649327  0.977803    33390496       175566
WeTrust              21296301  0.231111    92147500       251720
Wings                36238040  0.403954    89708333       425818
Xaurum               30743467  0.241862   127111604        74506
Yocoin                 720973  0.006826   105618830        87033

Tags: ethereum python