csbailey5t

Speaking in Code - Conference Notes

Bethany's Intro

An event long time in coming...

We are at a crossroads moment in DH, where there is a lot of pressure on traditional scholars to learn digital tools and methods. Obligation on DH developers to match that with better articulation of what does unspoken in their craft.

Large disjunct in this explosion of interest. Expectations and stereotypes from the larger industry (Silicon Valley) are being laid over humanities software developers. Also questions about relation of traditional humanities modes of work and interpretive concerns to digital humanities developers' work.

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UPDATE `omeka_users` SET `password`=sha1(concat(`salt`, 'NEW_PASSWORD')) WHERE `username`='USER';

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Keybase proof

I hereby claim:

• I am csbailey5t on github.
• I am csbailey5t (https://keybase.io/csbailey5t) on keybase.
• I have a public key whose fingerprint is BAE6 7D4F E7D9 E4E1 7993 A0AC BCDA 7043 DFE7 BDA5

To claim this, I am signing this object:

csbailey5t

for package in `ls node_modules/`; do npm uninstall $package; done;

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mysqldump courtesy of waynegraham

mysqldump -h [host] -u [user] --password=[password] [database] | gzip -c | cat > ~/Desktop/`date +%Y-%m-%d-%T`.sql.gz
mysqldump -h [host] -u [user] --password=[password] [database] | gzip -c | cat > ./`date +%Y-%m-%d-%T`.sql.gz

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mysqldump -h [host] -u [user] --password=[password] [database] | gzip -c | cat > ~/Desktop/`date +%Y-%m-%d-%T`.sql.gz
mysqldump -h [host] -u [user] --password=[password] [database] | gzip -c | cat > ./`date +%Y-%m-%d-%T`.sql.gz

csbailey5t

#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

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# coding: utf-8

# In[ ]:


#following code here: https://www.pyimagesearch.com/2017/06/19/image-difference-with-opencv-and-python/


# In[1]:

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import glob
import docx

def get_text(fn):
    doc = docx.Document(fn)
    fulltext = []
    for para in doc.paragraphs:
        fulltext.append(para.text)
    return "\n".join(fulltext)
    

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import pandas as pd

# Read in delimited file, setting delimiter, and keeping only single col of interest
df = pd.read_table("StanfordUniversity_TAX_201906_IL.txt", delimiter="|", usecols=["YEAR BUILT"])

# Check unique values
df["YEAR BUILT"].unique()

# Count the number of null/NaN values
df["YEAR BUILT"].isnull().sum()