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How to Conditionally Concat 2 Columns In Python Pandas Dataframe?

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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:

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.

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:

import pandas as pd import matplotlib.pyplot as plt

  1. Create a pandas dataframe with your data:

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:

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:

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':

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:

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:

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:

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

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