Pandas DataFrame Ne Method

Introduction

In this tutorial, we will learn about the ne() method in pandas DataFrame. The ne() method is used to compare a DataFrame with other data structures element-wise, and it returns a DataFrame of bool values that represent the result of the comparison.

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Skills Graph

Import pandas and create a DataFrame

First, we need to import the pandas library and create a DataFrame. In this example, we will create a DataFrame with three columns: A, B, and C.

#importing pandas as pd
import pandas as pd

#creating the DataFrame
df = pd.DataFrame({"A": [200, 500], "B": [60, 250], "C": [150, 1]})

Compare DataFrame with a scalar value

Next, we can use the ne() method to compare the DataFrame with a scalar value. This will return a bool-type DataFrame where each element represents whether it is not equal to the scalar value.

print("--------The DataFrame is---------")
print(df)
print("----After applying ne method-----")
print(df.ne(200))

Compare DataFrame with a Series

We can also compare the DataFrame with a Series using the ne() method. This will return a bool-type DataFrame where each element represents whether it is not equal to the corresponding element in the Series.

print("--------The DataFrame is---------")
print(df)
series = pd.Series([150, 200, 150])
print("----After applying ne method-----")
print(df.ne(series, axis=0))

Compare DataFrame with another DataFrame

Finally, we can compare the DataFrame with another DataFrame. This will return a bool-type DataFrame where each element represents whether it is not equal to the corresponding element in the other DataFrame.

print("----After applying ne method-----")
df_1 = pd.DataFrame({"A": [200, 500], "B": [60, 250], "C": [150, 1]})
df_2 = pd.DataFrame({"A": [200, 550], "B": [65, 251], "C": [100, 10]})
print(df_1.ne(df_2))

Summary

In this tutorial, we learned how to use the ne() method in pandas DataFrame. We saw how to compare a DataFrame with a scalar value, a Series, and another DataFrame. The ne() method is helpful in performing element-wise comparisons and obtaining bool-type DataFrames as a result. This helps in various data analysis and manipulation tasks.