Pandas Check Column Contains a Value in DataFrame

You can check if a column contains/exists a particular value (string/int), list of multiple values in pandas DataFrame by using pd.series(), in operator, pandas.series.isin(), str.contains() methods and many more.

In this article, I will explain how to check if a column contains a particular value with examples. These return True when a value contains in a specified column, False when not found.

Key Points –

The isin() method is a simple way to check if a column contains any value from a list, returning a boolean Series.
For partial string matches or substring checks, use the str.contains() method, which can also handle regex for pattern matching.
The unique() method provides the unique values in a column, which can be useful when checking for the presence of a specific value.
Accessing a column as a NumPy array using .values allows checking for a value directly with Python’s in operator.
When using str.contains(), you can control case sensitivity by passing the case parameter (e.g., case=False for case-insensitive matching).
Using methods like isin() and str.contains() returns a boolean Series, which can be used for filtering rows directly.

Quick Examples of Pandas Column Contains Particular value of DataFrame

If you are in a hurry, below are some quick examples of how to check if a pandas DataFrame column contains/exists a particular string value or a list of values.


# Quick examples of pandas column contains a value

# Check Column Contains a Value in DataFrame
print('Spark' in df['Courses'].unique())

# Check Column Contains a Value in DataFrame
print('Spark' in set(df['Courses']))

# Using DataFrame.values
print('Spark' in df['Courses'].values)

# Check column contains Particular value of DataFrame 
# Using Pandas.Series.isin()
print(df['Courses'].isin(['Spark','Python']))

# Column contains in multiple strings.
print(df[df['Courses'].str.contains('ark')])

Now, let’screate a pandas DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.


# Create a DataFrame.
import pandas as pd
technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print("Create DataFrame:\n", df)

Yields below output.

Check Column Contains a Value in DataFrame

Use in operator on a Series to check if a column contains/exists a string value in a pandas DataFrame. df['Courses'] returns a Series object with all values from column Courses, pandas.Series.unique will return unique values of the Series object. Uniques are returned in order of appearance. The unique technique is based on hash tables. in operators return True when a value is found in a Series object.

In the below example, the unique values in the 'Courses' column using the unique() method. Then, it checks if the string 'Spark' is present in the array of unique values and prints the result.


# Check the value of index by unique values.
print('Spark' in df['Courses'].unique())

# Output:
# True

We can use the in & not in operators on these values to check if a given element exists or not. For instance, first, create a set of unique values in the 'Courses' column using the set() function. Then, it checks if the string 'Spark' is present in the set of unique values and prints the result.


# Check the value of index by in parameter.
print('Spark' in set(df['Courses']))

# Output:
# True

You can also check using in operator with pandas.DataFrame.values. This returns numpy.ndarray. For instance, this program directly checks if the string 'Spark' is present in the underlying NumPy array (values) of the 'Courses' column. If 'Spark' is present in the values, the print statement will output True; otherwise, it will output False.


# Series can check the value in index by DataFrame.values.
print('Spark' in df['Courses'].values)

# Output:
# True

Using pandas.Series.isin() to Check Column Contains Value

Pandas.Series.isin() function is used to check whether a column contains a list of multiple values. It returns a boolean Series showing each element in the Series matches an element in the passed sequence of values exactly.

In the below example, contains_spark will be a boolean Series where each element indicates whether the corresponding value in the 'Courses' column is equal to 'Spark', or 'Python'.


# Check column contains Particular value of DataFrame by Pandas.Series.isin()
contains_spark = df['Courses'].isin(['Spark','Python'])
print(contains_spark)

# Output:
# r1     True
# r2    False
# r3     True
# r4    False
# Name: Courses, dtype: bool

Series.Str.contains() to Check Part of a value in Column

You can see how we can determine a pandas column contains a particular value of DataFrame using Series.Str.contains(). This contains() function is used to test the pattern or regex is contained within a string of a Series or Index.


# Column contains particular value by multiple strings.
print(df[df['Courses'].str.contains('ark')])

# Output:
#     Courses    Fee Duration  Discount
# r1    Spark  20000   30days      1000
# r2  PySpark  25000   40days      2300

Complete examples of Checking Column Contain a Particular Value


# Create a DataFrame
import pandas as pd

technologies = {
    'Courses':["Spark","PySpark","Python","pandas"],
    'Fee' :[20000,25000,22000,30000],
    'Duration':['30days','40days','35days','50days'],
    'Discount':[1000,2300,1200,2000]
              }
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)

# Check the value of index by unique values
print('Spark' in df['Courses'].unique())

# Check the value of index by in parameter
print('Spark' in set(df['Courses']))

# Series can check the value in index by DataFrame.values
print('Spark' in df['Courses'].values)

# Check column contains Particular value of DataFrame by Pandas.Series.isin()
print(df['Courses'].isin(['Spark','Python']))

# Column contains particular value by multiple strings
print(df[df['Courses'].str.contains('ark')])

Frequently Asked Questions on Pandas contains Column Value

To check if a specific value exists in a column of a Pandas DataFrame, you can use the isin() method.

You can check if multiple values exist in a column using the isin() method in pandas. For example, contains_values will be a boolean Series where each element indicates whether the corresponding value in the 'Courses' column is equal to either 'Python' or 'Java'.

If you want to check if a column contains a substring, you can use the str.contains() method in pandas. For example, contains_substring will be a boolean Series where each element indicates whether the substring 'Spark' is present in the corresponding value of the 'Courses' column.

You can perform a case-insensitive check using the str.contains() method in pandas. You can achieve this by setting the case parameter to False.

To negate the condition and filter rows where the column does not contain a specific value, you can use the ~ (tilde) operator along with the condition.

To check if any value in a column is missing (NaN), you can use the isna() method in pandas. For example, contains_missing will be a boolean Series where each element indicates whether the corresponding value in the 'Courses' column is missing (NaN).

Conclusion

In this article, you have learned how to check if a DataFrame column contains/exists a part of a value with examples by using in & not in operators, pandas.Series.isin() and also check if multiple elements exist in DataFrame.

References

https://www.geeksforgeeks.org/check-if-a-value-exists-in-a-dataframe-using-in-not-in-operator-in-python-pandas/