Check running servers:
jupyter notebook listStop running servers (both works):
jupyter notebook stop
kill $(pgrep jupyter)Source:
https://github.com/jupyter/notebook/issues/1950
https://stackoverflow.com/questions/10162707/how-to-close-ipython-notebook-properly/32745046#32745046
with open('file1.txt','r') as f1, open('file2.txt','r') as f2:
for line1, line2 in zip(f1, f2):
if line1 != line2:
print(line1,'\n',line2)In Jupyter Notebook, use the below command within the notebook to install packages in the current working environment. This is specifically useful when you are working in the notebook, and need to install new packages to the matching python version you are using.
!{sys.executable} -m pip install XXX
This is a solution in python version (3.0+) using a package called pyBigWig to extract a given genomic region from a whole genome bigwig file.
Prepare your input bigwig file:
import pyBigWig
# First open bigwig file
bwIn = pyBigWig.open('input.bw')
# check bigwig file header
print(bwIn.header())Prepare output, since your output doesn’t have a header, you need to add the header using the chosen chromosome size, here I’m using a region from chr18 as an example.
bwOutput = pyBigWig.open('output.bw','w')
bwOutput.addHeader([('chr18',80373285)]) # chromosome size
for x in bwIn.intervals('chr18',62926563,63516911):
bwOutput.addEntries(['chr18'],[x[0]],ends=[x[1]],values=[x[2]])
bwOutput.close()data = [False, False, False]
# Return any True in the list
any(data)
# Return any False in the list
not any(data)
# Same goes for all values True in the list
all(data)With value to find the matching key:
# function to return key for any value
def get_key(my_dict, val):
for key, value in my_dict.items():
if val in value: # when your value is a list
# if val == value: # when your value is a string
return key
return "key doesn't exist"Source:
https://www.geeksforgeeks.org/python-get-key-from-value-in-dictionary/
When you have a table with many columns but only want to rearrange or swap two or a few columns.
cols = list(df.columns)
a, b = cols.index('XXXX'), cols.index('YYYY')
cols[b], cols[a] = cols[a], cols[b]
df = df[cols]Source:
https://stackoverflow.com/questions/53141240/pandas-how-to-swap-or-reorder-columns
Sometimes you want to save a dictionary into a reusable file, and reuse it later. This is specifically useful when you need to save a color scheme for a large category and want to keep it consistent across your whole project.
Output it as a file:
import pickle
colorDicOut = open('colorOut_dic.pkl', 'wb')
pickle.dump(colorDic, colorDicOut)
colorDicOut.close()
with open('colorOut_dic.pkl', 'wb') as colorDicOut:
pickle.dump(colorDic, colorDicOut)Load it into a variable:
colorDicIn = open("colorOut_dic.pkl","rb")
colorDic = pickle.load(colorDicIn)If
UnicodeDecodeErrorcomes up, then it is likely an issue of incompatibility between python2 and python3, please refer to this post for solutions.
Source:
https://stackoverflow.com/questions/11218477/how-can-i-use-pickle-to-save-a-dict
Replace a specific value with another value in a dataframe:
df.replace(originalValue, newValue, inplace=True)
df = df.replace(originalValue, newValue)Replace values not equal to a given value with another value:
df[df != originalValue] = newValue