How to Conditionally Concat 2 Columns In Python Pandas Dataframe?

8 minutes read

You can conditionally concat two columns in a pandas dataframe using the np.where function.


Here is an example code snippet that demonstrates how to achieve this:

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import pandas as pd
import numpy as np

# Create a sample dataframe
data = {'A': [1, 2, 3, 4],
        'B': [10, 20, 30, 40]}

df = pd.DataFrame(data)

# Conditionally concatenate columns A and B
df['C'] = np.where(df['A'] > df['B'], df['A'].astype(str) + '_' + df['B'].astype(str), df['A'])

print(df)


In this code snippet:

  • We import the pandas library as pd and the numpy library as np.
  • We create a sample dataframe with columns A and B.
  • We use the np.where function to conditionally concatenate the values in columns A and B based on a specified condition.
  • The result is stored in a new column called C in the dataframe.

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How to plot data from a pandas dataframe using matplotlib?

To plot data from a pandas dataframe using matplotlib, you can follow these steps:

  1. First, import the necessary libraries:
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import pandas as pd
import matplotlib.pyplot as plt


  1. Create a pandas dataframe with your data:
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data = {'x': [1, 2, 3, 4, 5],
        'y': [10, 15, 13, 18, 16]}
df = pd.DataFrame(data)


  1. Use the plot() method of the pandas dataframe to create a basic plot:
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df.plot(x='x', y='y', kind='line')
plt.show()


This will generate a line plot with the specified x and y columns from the dataframe.


You can also customize the plot by adding labels, titles, legends, changing colors, and more using matplotlib functions. For example:

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plt.plot(df['x'], df['y'], marker='o', color='orange', linestyle='--')
plt.xlabel('X-axis label')
plt.ylabel('Y-axis label')
plt.title('Plot Title')
plt.legend(['Data points'])
plt.grid(True)
plt.show()


These steps will help you plot data from a pandas dataframe using matplotlib.


How to calculate the median value of a column in a pandas dataframe?

You can calculate the median value of a column in a pandas dataframe using the median() method. Here is an example of how to calculate the median value of a column named 'column_name' in a pandas dataframe called 'df':

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median_value = df['column_name'].median()
print("Median value of the column:", median_value)


This will calculate the median value of the specified column and store it in the variable median_value. You can then print or use this value as needed.


How to drop columns in a pandas dataframe?

To drop columns in a pandas dataframe, you can use the drop() method along with the axis parameter set to 1 for columns. Here's an example:

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import pandas as pd

# Create a sample dataframe
data = {'A': [1, 2, 3, 4],
        'B': [5, 6, 7, 8],
        'C': [9, 10, 11, 12]}
df = pd.DataFrame(data)

# Drop columns 'B' and 'C'
df = df.drop(['B', 'C'], axis=1)

print(df)


This will output:

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   A
0  1
1  2
2  3
3  4


In this example, the columns 'B' and 'C' were dropped from the dataframe.


What is the syntax for selecting multiple columns in a pandas dataframe?

To select multiple columns in a pandas DataFrame, you can use the following syntax:

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df[['column1', 'column2', 'column3']]


Where df is the DataFrame and 'column1', 'column2', and 'column3' are the names of the columns you want to select.

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