stripping notebook info

importSACCTinfo.ipynb

 %% Cell type:code id: tags:
 
-``` python
+``` 
 import numpy as np
 import pandas as pd
 import pandas_profiling
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 df = pd.read_csv('userusage.txt',delimiter='|')
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 df.head()
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 df[['jid','step']] = df.JobID.str.split(".",expand=True)
 df.Partition.values
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 batchDF=df.dropna(subset=["MaxRSS"])
 userDF=df.dropna(subset=["User"])
 for jid in df.jid.unique():
     userDF['MaxRSS'][userDF['jid'] == jid]=batchDF['MaxRSS'][batchDF['jid'] == jid]
 
     #print(userDF[userDF['jid'] == jid])
 
 userDF.head()
 ```
 
 %% Cell type:markdown id: tags:
 
 # add more graphs here
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 ```

slurm-2sql.ipynb

 %% Cell type:code id: tags:
 
-``` python
+``` 
 import sqlite3
 import slurm2sql
 import pandas as pd
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 db = sqlite3.connect('test.db')
 slurm2sql.slurm2sql(db, ['-S', '2020-03-18', '-a'])
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 # For example, you can then convert to a dataframe:
 df1 = pd.read_sql('SELECT * FROM slurm', db)
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 df1.head(5)
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 print("more plots to come")
 ```
 
 %% Cell type:code id: tags:
 
-``` python
+``` 
 ```