You can select the Rows from Pandas DataFrame based on column values or based on multiple conditions either using DataFrame.loc[] attribute, DataFrame.query() or DataFrame.apply() method to use lambda function. In this article, I will explain how to select rows based on single or multiple column values (values from the list) and also how to select rows that have no None or Nan values.
1. Quick Examples of Select Rows Based on Column Values
If you are in hurry, below are some examples of how to select rows based on column values in pandas DataFrame.
# Select Rows Based on column Values
df[df["Courses"] == 'Spark']
df.loc[df['Courses'] == value]
df.query("Courses == 'Spark'")
df.loc[df['Courses'] != 'Spark']
df.loc[df['Courses'].isin(values)]
df.loc[~df['Courses'].isin(values)]
# Select Multiple Conditions using Multiple Columns
df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)]
df.loc[(df['Discount'] >= 1200) & (df['Fee'] >= 23000 )]
# Using lambda function
df.apply(lambda row: row[df['Courses'].isin(['Spark','PySpark'])])
# Select columns that have no None & nana values
df.dropna()
# Other examples
df[df['Courses'].str.contains("Spark")]
df[df['Courses'].str.lower().str.contains("spark")]
df[df['Courses'].str.startswith("P")]
If you are a learner, Let’s see with sample data and run through these examples and explore the output to understand better. First, create a panda DataFrame from Dict.
import pandas as pd
import numpy as np
technologies= {
'Courses':["Spark","PySpark","Hadoop","Python","Pandas"],
'Fee' :[22000,25000,23000,24000,26000],
'Duration':['30days','50days','30days', None,np.nan],
'Discount':[1000,2300,1000,1200,2500]
}
df = pd.DataFrame(technologies)
print(df)
Note that the above DataFrame also contains None and Nan values on Duration column that I would be using in my examples below to select rows that has None & Nan values or select ignoring these.
2. Select Rows Based on Column Values
You can use df[df["Courses"] == 'Spark'] to select rows. Not that this expression returns a new DataFrame with selected rows.
df2=df[df["Courses"] == 'Spark']
print(df2)
Yields below output
Courses Fee Duration Discount
0 Spark 22000 30days 1000
You can also write the above statement with a variable.
value="Spark"
df2=df[df["Courses"] == value]
If you wanted to select based on column value not equals then use != operator. df[df["Courses"] != 'Spark']
df[df["Courses"] != 'Spark']
3. Using DataFrame.query()
Using query() method you can filter Pandas DataFrame rows using an expression, below is a simple example. You can use query() pretty much to run any example explained in this article.
df2=df.query("Courses == 'Spark'")
print(df2)
Yields same output as above. You can also try other examples explained above with this approach.
4. Using DataFrame.loc to select based on Column Values
By using DataFrame.loc[].
df2=df.loc[df['Courses'] == "Spark"]
print(df2)
Yields same output as above. You can also try other examples explained above with this approach.
5. Select Rows Based on List of Column Values
If you have values in a list and wanted to select the rows based on the list of values use isin() method.
# Select Rows Based on List of Column Values
values=["Spark","PySpark"]
print(df[df["Courses"].isin(values)] )
# Using df.loc
print(df.loc[df['Courses'].isin(values)])
Yields below output.
Courses Fee Duration Discount
0 Spark 22000 30days 1000
1 PySpark 25000 50days 2300
Select rows from not in a list of column values can be done using ~ operator
df2=df.loc[~df['Courses'].isin(values)]
print(df2)
6. Using Multiple Column Conditions
Most of the time we would need to select the rows based on multiple conditions applying on multiple columns, you can do that in Pandas as below. Note that the parentheses are needed for each condition expression due to Python’s operator precedence rules. & operator binds more tightly than <= and >=. not using parenthesis will have unexpected results.
# Select Rows based on multiple conditions
print(df.loc[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)])
print(df[(df['Discount'] >= 1000) & (df['Discount'] <= 2000)])
print(df.query("Discount >= 1000 & Discount <= 2000"))
Yields below output.
Courses Fee Duration Discount
0 Spark 22000 30days 1000
2 Hadoop 23000 30days 1000
3 Python 24000 None 1200
You can also use multiple columns to select Pandas DataFrame rows.
df2=df.loc[(df['Discount'] >= 1200) & (df['Fee'] >= 23000 )]
print(df2)
Yields below output
Courses Fee Duration Discount
1 PySpark 25000 50days 2300
3 Python 24000 None 1200
4 Pandas 26000 NaN 2500
7. Select Rows Using DataFrame.apply()
DataFrame.apply() method is used to apply the lambda function row-by-row and return the rows that matched with the values.
# By using lambda function
print(df.apply(lambda row: row[df['Courses'].isin(['Spark','PySpark'])]))
Yields below output.
Courses Fee Duration Discount
0 Spark 22000 30days 1000
1 PySpark 25000 50days 2300
8. Using With None & nan Data
In case you wanted to drop rows that have None or nan on column values, use DataFrame.dropna() method.
# select rows by ignoreing columns that have None & Nan values
print(df.dropna())
Yields below output
Courses Fee Duration Discount
0 Spark 22000 30days 1000
1 PySpark 25000 50days 2300
2 Hadoop 23000 30days 1000
In case if you wanted to drop columns when column values are None or nan. To delete columns, I have covered some examples on how to drop Pandas DataFrame columns
print(df.dropna(axis='columns'))
Yields below output.
Courses Fee Discount
0 Spark 22000 1000
1 PySpark 25000 2300
2 Hadoop 23000 1000
3 Python 24000 1200
4 Pandas 26000 2500
9. Other Examples of Select Rows Based On Column Values
# Select based on value contains
print(df[df['Courses'].str.contains("Spark")])
# Select after converting values
print(df[df['Courses'].str.lower().str.contains("spark")])
#Select startswith
print(df[df['Courses'].str.startswith("P")])
Conclusion
In this article, I have explained 10 different examples to select Pandas Rows based on column values. Remember that when you select DataFrame Rows, it always returns a new DataFrame with selected rows. I hope this article helps you learn Pandas.
Happy Learning !!
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