Looping Through Multiple Columns in a Pandas DataFrame

When working with large datasets, it’s often necessary to perform calculations on individual columns or groups of columns. In this article, we’ll explore how to loop through multiple columns in a pandas DataFrame and apply formulas to each column.

Introduction to Pandas DataFrames

A pandas DataFrame is a two-dimensional table of data with rows and columns. It provides efficient data structures and operations for manipulating numerical data. The DataFrame is the core data structure in pandas, and it’s used extensively in data analysis, scientific computing, and data science.

Looping Through Columns in a DataFrame

To loop through multiple columns in a DataFrame, you can use various methods such as indexing, iterating over column names, or using list comprehensions. In this article, we’ll focus on the latter approach.

collist = list(orignal_dataframe.columns)

This line of code creates a list called collist that contains all the column names in the original DataFrame.

Creating an Empty List and Dictionary

To store the results of our calculations for each column, we need to create an empty list and dictionary. The list will hold dictionaries where each dictionary represents a single row in our output DataFrame.

emptylist = []
emptydict = {}

The emptylist is an empty list that will be used to store the dictionaries representing individual rows in our output DataFrame. The emptydict is an empty dictionary that will be used as a key for each column in our output DataFrame.

Loops and Calculations

We’ll use nested loops to iterate through each column in our original DataFrame. The outer loop will iterate over all columns, while the inner loop will calculate the formula for each column.

for i in collist[7:]:
    B1 = (((gdf.groupby(['Benthic_Mo'])['SHAPE_Area'].sum())/Area_sum) *  (gdf.groupby(['Benthic_Mo'])[i].mean())).sum()
    emptydict[i]  = B1

In this inner loop, we calculate the formula for each column i by grouping our DataFrame gdf by 'Benthic_Mo', summing up the values in 'SHAPE_Area', and then multiplying the result by the mean value of column i. We store the result in the emptydict.

Creating an Output DataFrame

After looping through all columns, we append the emptydict to our emptylist. Finally, we create a new DataFrame from our emptylist using the following line of code:

resdf = pd.DataFrame(emptylist)

This creates a new DataFrame with one row and as many columns as there are original columns in our DataFrame.

Calculating Variance or Standard Deviation

To calculate variance or standard deviation for each column, we can use the var() function provided by pandas DataFrames. Here’s an example of how to do this:

for i in collist[7:]:
    B1 = (((gdf.groupby(['Benthic_Mo'])['SHAPE_Area'].sum())/Area_sum) *  (gdf.groupby(['Benthic_Mo'])[i].mean())).sum()
    std_dev = B1.var() ** 0.5
    emptydict[i]  = std_dev

In this example, we calculate the variance of each column i by calling the var() function on our calculated value B1. We then take the square root of the result to get the standard deviation.

Conclusion

Looping through multiple columns in a pandas DataFrame is a useful technique when working with large datasets. By using list comprehensions and nested loops, we can easily apply formulas to each column and create an output DataFrame with one row and as many columns as there are original columns. Additionally, calculating variance or standard deviation for each column can provide valuable insights into the data.

Full Code Example

Here’s the full code example that combines all the techniques discussed in this article:

import pandas as pd

# Create a sample DataFrame
gdf = pd.DataFrame({
    'OBJECTID': [1, 2, 3],
    'Benthic_Mo': ['A', 'A', 'B'],
    'SHAPE_Leng': [10, 20, 30],
    'SHAPE_Area': [100, 200, 300],
    'geometry': ['x', 'y', 'z'],
    'tmp': [1, 2, 3],
    'Species': ['A', 'B', 'C'],
    'W8_629044': [10, 20, 30],
    'W8_642938': [100, 200, 300],
    'W8_656877': [1000, 2000, 3000],
    'W8_670861': [10000, 20000, 30000],
    'W8_684891': [100000, 200000, 300000],
    'W8_698965': [1000000, 2000000, 3000000],
    'W8_713086': [10000000, 20000000, 30000000],
    'W8_72726': [100000000, 200000000, 300000000]
})

Area_sum = gdf['SHAPE_Area'].sum()

# Create an empty list and dictionary
emptylist = []
emptydict = {}

# Loop through columns in the DataFrame
collist = list(gdf.columns)
for i in collist[7:]:
    B1 = (((gdf.groupby(['Benthic_Mo'])['SHAPE_Area'].sum())/Area_sum) *  (gdf.groupby(['Benthic_Mo'])[i].mean())).sum()
    emptydict[i]  = B1
    emptylist.append(emptydict)

# Create an output DataFrame
resdf = pd.DataFrame(emptylist)

# Calculate variance or standard deviation for each column
for i in collist[7:]:
    B1 = (((gdf.groupby(['Benthic_Mo'])['SHAPE_Area'].sum())/Area_sum) *  (gdf.groupby(['Benthic_Mo'])[i].mean())).sum()
    std_dev = B1.var() ** 0.5
    emptydict[i]  = std_dev

# Create an output DataFrame with variance or standard deviation
resdf['variance'] = resdf.map(lambda x: x[1])

Last modified on 2023-05-11