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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Transect
Groupby and transform allow me to combine rows into a single 'transect' row.
Or, use a multiIndex, a hierarchical index, so I can target specific cells using id and type. The index item for a multiIndex is a TUPLE.
Step1:  shift data to correct column
using loc for assignment
Step2:  use groupby and transform to fill the row
Step3:  shift data to correct row using a multi-Index
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Python Code:
%matplotlib inline
import sys
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
from io import StringIO
print(sys.version)
print("Pandas:", pd.__version__)
df = pd.read_csv('C:/Users/Peter/Documents/atlas/atlasdata/obs_types/transect.csv', parse_dates=['date'])
df = df.astype(dtype='str')# we don't need numbers in this dataset.
df=df.replace('nan','')
#this turns dates into strings with the proper format for JSON:
#df['date'] = df['date'].dt.strftime('%Y-%m-%d')
df.type = df.type.str.replace('\*remonitoring notes','transect')
df.type = df.type.str.replace('\*plot summary','transect')
Explanation: Transect
Groupby and transform allow me to combine rows into a single 'transect' row.
Or, use a multiIndex, a hierarchical index, so I can target specific cells using id and type. The index item for a multiIndex is a TUPLE.
End of explanation
df.loc[df.type =='map',['mapPhoto']]=df['url'] #moving cell values to correct column
df.loc[df.type.str.contains('lineminus'),['miscPhoto']]=df['url']
df.loc[df.type.str.contains('lineplus'),['miscPhoto']]=df['url']
df.loc[df.type.str.contains('misc'),['miscPhoto']]=df['url']
#now to deal with type='photo'
photos = df[df.type=='photo']
nonphotos = df[df.type != 'photo'] #we can concatenate these later
grouped = photos.groupby(['id','date'])
photos.shape
values=grouped.groups.values()
for value in values:
    photos.loc[value[2],['type']] = 'misc'
    #photos.loc[value[1],['type']] = 'linephoto2'
photos.loc[photos.type=='linephoto1']
for name, group in grouped:
    print(grouped[name])
photos = df[df.type == 'photo']
photos.set_index(['id','date'],inplace=True)
photos.index[1]
photos=df[df.type=='photo']
photos.groupby(['id','date']).count()
photos.loc[photos.index[25],['type','note']]
#combine photo captions
df['caption']=''
df.loc[(df.type.str.contains('lineminus'))|(df.type.str.contains('lineplus')),['caption']]=df['type'] + ' | ' + df['note'] 
df.loc[df.type.str.contains('lineplus'),['caption']]=df['url']
df.loc[df.type.str.contains('misc'),['caption']]=df['url']
df['mystart'] = 'Baseline summary:'
df.loc[df.type =='transect',['site_description']]= df[['mystart','label1','value1','label2','value2','label3','value3','note']].apply(' | '.join, axis=1)
df.loc[df.type.str.contains('line-'),['linephoto1']]=df['url']
df.loc[df.type.str.contains('line\+'),['linephoto2']]=df['url']#be sure to escape the +
df.loc[df.type.str.contains('linephoto1'),['linephoto1']]=df['url']
df.loc[df.type.str.contains('linephoto2'),['linephoto2']]=df['url']
df.loc[df.type == 'plants',['general_observations']]=df['note']
Explanation: shift data to correct column
using loc for assignment: df.loc[destination condition, column] = df.loc[source]
End of explanation
#since we're using string methods, NaNs won't work
mycols =['general_observations','mapPhoto','linephoto1','linephoto2','miscPhoto','site_description']
for item in mycols:
    df[item] = df[item].fillna('')
df.mapPhoto = df.groupby('id')['mapPhoto'].transform(lambda x: "%s" % ''.join(x))
df.linephoto1 = df.groupby(['id','date'])['linephoto1'].transform(lambda x: "%s" % ''.join(x))
df.linephoto2 = df.groupby(['id','date'])['linephoto2'].transform(lambda x: "%s" % ''.join(x))
df.miscPhoto = df.groupby(['id','date'])['miscPhoto'].transform(lambda x: "%s" % ''.join(x))
df['site_description'] = df['site_description'].str.strip()
df.to_csv('test.csv')
#done to here. Next, figure out what to do with linephotos, unclassified photos, and their notes.
#make column for photocaptions. When adding linephoto1, add 'note' and 'type' fields to caption column. E.g. 'linephoto1: 100line- | view east along transect.' Then join the rows in the groupby transform and add to site_description field.
df.shape
df[(df.type.str.contains('line\+'))&(df.linephoto2.str.len()<50)]
maps.str.len().sort_values()
Explanation: use groupby and transform to fill the row
End of explanation
ids = list(df['id'])#make a list of ids to iterate over, before the hierarchical index
#df.type = df.type.map({'\*plot summary':'transect','\*remonitoring notes':'transect'})
df.loc[df.type =='map',['mapPhoto']]=df['url'] #moving cell values to correct column
df.set_index(['id','type'],inplace=True) # hierarchical index so we can call locations
#a hierarchical index uses a tuple. You can set values using loc.
#this format: df.loc[destination] = df.loc[source].values[0]
for item in ids:
    df.loc[(item,'*plot summary'),'mapPhoto'] = df.loc[(item,'map'),'mapPhoto'].values[0]
#generates a pink warning about performance, but oh well.
#here we are using an expression in parens to test for a condition
(df['type'].str.contains('\s') & df['note'].notnull()).value_counts()
df.url = df.url.str.replace(' ','_');df.url
df.url.head()
df['newurl'] = df.url.str.replace
df.newurl.head()
#for combining rows try something like this:
print(df.groupby('somecolumn')['temp variable'].apply(' '.join).reset_index())
Explanation: shift data to correct row using a multi-Index
End of explanation
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