#Video Playlist: https://www.youtube.com/playlist?list=PLHae9ggVvqPgyRQQOtENr6hK0m1UquGaG

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#Basic plotting of data from pandas dataframe.

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import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')

#Pandas works with Matplotlib in the background. SO we don't have to import Pyplot for basic plotting.

#To plot single histogram based on single value

#df['Manual'].plot(kind='hist', title='Manual Count')

df['Manual'].plot(kind='hist', title='Manual Count', bins=30, figsize=(12,10)) #Can also add bins and fig size

#To work only with Set 1 data we can create a new dataframe for that specific set

#and work with that dataframe.

#Let us plot only Set 1.

import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')

#Change Unnamed: 0 name to Image_set

df = df.rename(columns = {'Unnamed: 0':'Image_set'})

print(df.columns)

set1_df = df[df['Image_set'] == 'Set1']

set1_df['Manual'].plot()

#Let's go back to all sets now.

import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')

#Change Unnamed: 0 name to Image_set

df = df.rename(columns = {'Unnamed: 0':'Image_set'})

#df['Manual'].plot()

# Sometimes you need to smooth data for better visualization.

#One way to Smooth is by averaging few points

df['Manual'].rolling(3).mean().plot() #MUch nicer plot.

#Can do rolling mean or sum or anything else that makes sense.

#Some disconnects, let's not worry about it for now.

#We can also graphically represent the statistics.

#do you remember df['Manual'].describe()

import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')

#Change Unnamed: 0 name to Image_set

df = df.rename(columns = {'Unnamed: 0':'Image_set'})

print(df['Manual'].describe())

#This can be graphically represented using box plot.

#df['Manual'].plot(kind="box", figsize=(8,6)) #Shows max and min values, outliers, etc.

#In order to plot the relationship between Columns, we typically generate scatter plots

df.plot(kind='scatter', x='Manual', y='Auto_th_2', title='Manual vs Auto2')

#### Using functions and creating Caterogries and plotting

#Now, Let's go through an exercise where we define all cell counts below 100 as low

#and above as high. Then let's plot using the new categories we defined.

import pandas as pd

df = pd.read_csv('data/manual_vs_auto.csv')

#Change Unnamed: 0 name to Image_set

df = df.rename(columns = {'Unnamed: 0':'Image_set'})

#Let's define a function to categorize low and high counts.

def cell_count(x):

return "high"

#Now we want to send the entire Manual column through this function, which is what apply() does:

#Start by defining a new column title cell_count_index

#Then just apply the function to categorize counts into low and high.

df["cell_count_index"] = df["Manual"].apply(cell_count)

print(df.head())

#Creates a new column called grain_category

#Can save to new csv

df.to_csv('data/manual_vs_auto2.csv')

print(df.loc[20:30, ['Manual', 'cell_count_index']]) #Verify 20-30 rows.

#we can plot by combining this cell count index information

df.boxplot(column='Manual', by='cell_count_index')