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Multi-Index Combine Columns

05 Changing Index and Combining Columns in Pandas¶

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import pandas as pd

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# tuples are different
tuple1 = (1,2,3,4) # we create tuples with parentheses 

tuple2 = tuple1 # we copy the tuple1 into another variable tuple2

# When we add to tuple2, we are NOT changing the original tuple2 variable
# we are creating a NEW tuple that has the same elements as tuple2 with addition of 5, and assigning the NEW tuple to the variable tuple2
tuple2 += (5,) 

print(tuple1)
print(tuple2)

# A DataFrame can be created from a dictionary of tuples
# Each key-value pair in the dictionary corresponds to a column in the DataFrame
# The key is the column label and the tuple is the column data
data = {
    'mag': (1,1,3,3),
    'date': ('April 11 2024','April 11 2024','April 12 2024','April 13 2024'),
    'inj' : (1,2,100,200)
}
tdata = pd.DataFrame(data)

############################################33

### SIDE BAR - A mini lesson on Zip(): 
# Zip() is used to pair elements
            # say you have two lists
# Using zip to pair elements from two lists
list1 = ['a', 'b', 'c']
list2 = [1, 2, 3]
paired = list(zip(list1, list2))
print(paired)  # Output: [('a', 1), ('b', 2), ('c', 3)]

## END
#################################################

# Combine the 'the 'mag' and 'date' columns to tuples in a new column called key
tdata['key'] = list(zip(tdata['mag'], tdata['date']))

# Now you can use the 'key' column to group the data
grouped = tdata.groupby('key')['inj'].sum() # sum injuries for each unique combination of mag-date
print(tdata)
print(grouped)

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# Create a DataFrame
data = {
    'city': ['New York', 'Los Angeles', 'Chicago', 'New York', 'Los Angeles', 'Chicago'],
    'date': ['2021-01-01', '2021-01-01', '2021-01-01', '2021-02-01', '2021-02-01', '2021-02-01'],
    'temperature': [32, 75, 30, 30, 77, 30]
}
df = pd.DataFrame(data)



# Now let's use zip() to pair columns again

# Combine the 'city' and 'date' columns to tuples in a new column called Key
df['key'] = list(zip(df['city'], df['temperature']))

# Now you can use the 'key' column to group the data
grouped = df.groupby('key')['date'].nunique() # nunique counts the number of unique date per city-temp 

print(df) # before grouping
print(grouped)

# while the example is strange, combining columns into one is useful

# Why? : combining columns allows you to consider multiple columns in your analysis 
# with less computational and coding effort than grouping by multiple columns

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