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How to Get Percentage Of Total For Each Row In Pandas?

5 minutes read

To get the percentage of total for each row in a pandas DataFrame, you can use the apply function along with a custom lambda function. First, sum up the values in each row using the sum function. Then, divide each value in the row by the total sum and multiply by 100 to get the percentage. Here is an example code snippet:

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import pandas as pd

# Create a sample DataFrame
data = {'A': [10, 20, 30],
        'B': [5, 10, 15]}
df = pd.DataFrame(data)

# Calculate total sum of each row
total_sum = df.sum(axis=1)

# Calculate percentage of total for each row
percentage_df = df.apply(lambda x: (x / total_sum) * 100, axis=1)

print(percentage_df)


This will print a new DataFrame where each value has been converted to its percentage of the total sum for that row.


How to convert percentages to fractions in pandas?

You can convert percentages to fractions in pandas by dividing the percentage value by 100 and then converting it to a fraction using the Fraction class from the fractions module.


Here is an example code snippet to convert a column of percentages to fractions in a pandas DataFrame:

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import pandas as pd
from fractions import Fraction

# Create a sample DataFrame with a column of percentages
data = {'percentage': [25, 50, 75]}
df = pd.DataFrame(data)

# Convert the percentages to fractions
df['fraction'] = df['percentage'] / 100
df['fraction'] = df['fraction'].apply(lambda x: Fraction(x).limit_denominator())

print(df)


This code will output the following DataFrame:

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   percentage fraction
0         25    1/4
1         50    1/2
2         75    3/4


In this example, we first divide the percentage column by 100 to convert it to a decimal fraction. Then, we apply the limit_denominator() method from the Fraction class to convert the decimal fraction to a simplified fraction.


How to calculate cumulative percentages in pandas?

To calculate cumulative percentages in pandas, you can use the cumsum() function along with the sum() function. Here is an example:

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import pandas as pd

# Create a sample DataFrame
data = {'A': [10, 20, 30, 40, 50], 'B': [15, 25, 35, 45, 55]}
df = pd.DataFrame(data)

# Calculate the cumulative sum of column 'A'
df['cumulative_percentage_A'] = (df['A'] / df['A'].sum()).cumsum() * 100

# Calculate the cumulative sum of column 'B'
df['cumulative_percentage_B'] = (df['B'] / df['B'].sum()).cumsum() * 100

print(df)


This will display the original DataFrame along with two new columns cumulative_percentage_A and cumulative_percentage_B that contain the cumulative percentages for columns 'A' and 'B', respectively.


How to plot percentage values in pandas using visualization libraries?

To plot percentage values in pandas using visualization libraries, you can follow these steps:

  1. Ensure that you have the necessary libraries installed, such as pandas, matplotlib, and seaborn.
  2. Load your data into a pandas DataFrame.
  3. Calculate the percentage values that you want to plot. For example, you can calculate the percentage of a certain category within a dataset.
  4. Create a visualization using a library such as matplotlib or seaborn. You can use functions like barplot() or countplot() to plot the percentage values.
  5. Make sure to set the y-axis labels to display percentages. This can be done using matplotlib's FuncFormatter or by adjusting the y-axis tick labels directly.


Here is an example code snippet to plot percentage values in a barplot using seaborn:

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import pandas as pd
import seaborn as sns

# Create a sample DataFrame
data = {'category': ['A', 'B', 'C', 'D'],
        'count': [20, 30, 40, 10]}
df = pd.DataFrame(data)

# Calculate the percentage values
df['percentage'] = (df['count'] / df['count'].sum()) * 100

# Create a barplot using seaborn
sns.barplot(x='category', y='percentage', data=df)
sns.set(style='whitegrid')

# Set y-axis labels to show percentages
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter

def percentage(x, pos):
    return f'{x}%'

formatter = FuncFormatter(percentage)
plt.gca().yaxis.set_major_formatter(formatter)

plt.show()


This code snippet creates a barplot displaying the percentage values of each category in the DataFrame. The y-axis labels are set to display percentages using a custom formatter function. You can customize the visualization further by adjusting the color palette, labels, and plot aesthetics according to your needs.


How to calculate weighted percentages in pandas?

To calculate weighted percentages in pandas, you can use the following steps:

  1. First, create a DataFrame with two columns - one for the values you want to calculate the weighted percentage for, and one for the weights of each value.
  2. Next, calculate the total weight by summing up the weights column.
  3. Then, calculate the weighted values by multiplying the values column by the weights column.
  4. Finally, calculate the weighted percentage by dividing the weighted values by the total weight and multiplying by 100.


Here's an example code snippet to demonstrate this:

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import pandas as pd

# Create a sample DataFrame
data = {'values': [10, 20, 30, 40],
        'weights': [0.1, 0.2, 0.3, 0.4]}
df = pd.DataFrame(data)

# Calculate total weight
total_weight = df['weights'].sum()

# Calculate weighted values
df['weighted_values'] = df['values'] * df['weights']

# Calculate weighted percentages
df['weighted_percentage'] = (df['weighted_values'] / total_weight) * 100

print(df)


This will output a DataFrame with the weighted values and percentages calculated for each value in the data.


How to normalize data using percentages in pandas?

To normalize data using percentages in pandas, follow these steps:

  1. Load your data into a pandas DataFrame.
  2. Identify the columns you want to normalize as percentages.
  3. Calculate the total sum for each row or column in the DataFrame.
  4. Divide each value in the selected columns by the total sum and multiply by 100 to get the percentage.
  5. Replace the original values with the normalized percentage values.


Here's an example code snippet to demonstrate how to normalize data in a DataFrame using percentages:

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import pandas as pd

# Create a sample DataFrame
data = {'A': [10, 20, 30, 40],
        'B': [50, 60, 70, 80]}
df = pd.DataFrame(data)

# Calculate total sum for each row
total_sum = df.sum(axis=1)

# Normalize the data in columns 'A' and 'B' as percentages
df['A_percentage'] = (df['A'] / total_sum) * 100
df['B_percentage'] = (df['B'] / total_sum) * 100

# Print the normalized DataFrame
print(df)


This code snippet will add two new columns ('A_percentage' and 'B_percentage') to the DataFrame with the normalized data in percentages. You can adjust the code to fit your specific data and requirements.

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