You can use DataFrame properties loc[], iloc[], at[], iat[] and other ways to get/select a cell value from a Pandas DataFrame. Pandas DataFrame is structured as rows & columns like a table, and a cell is referred to as a basic block that stores the data. Each cell contains information relating to the combination of the row and column headings.
loc[] & iloc[] are also used to select rows from pandas DataFrame and select columns from pandas DataFrame.
1. Quick Examples of Get Cell Value of DataFrame
If you are in a hurry, below are some of the quick examples of how to select cell values from pandas DataFrame.
# Belwo are quick example
# Using loc[]. Get cell value by name & index
print(df.loc[3]['Duration'])
print(df.loc[3][2])
# Using iloc[]. Get cell value by index & name
print(df.iloc[3]['Duration'])
print(df.iloc[3,2])
# Using DataFrame.at[]
print(df.at[3,'Duration'])
print(df.at[df.index[3],'Duration'])
# Using DataFrame.iat[]
print(df.iat[3,2])
#Get a cell value
print(df["Duration"].values[3])
# Get cell value from last row
print(df.iloc[-1,2])
print(df.iloc[-1]['Duration'])
print(df.at[df.index[-1],'Duration'])
Now, let’s create a DataFrame with a few rows and columns and execute some examples and validate the results. Our DataFrame contains column names Courses, Fee, Duration, Discount.
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python","pandas"],
'Fee' :[24000,25000,25000,24000,24000],
'Duration':['30day','50days','55days', '40days','60days'],
'Discount':[1000,2300,1000,1200,2500]
}
df = pd.DataFrame(technologies)
print(df)
Yields below output.
Courses Fee Duration Discount
0 Spark 24000 30day 1000
1 PySpark 25000 50days 2300
2 Hadoop 25000 55days 1000
3 Python 24000 40days 1200
4 pandas 24000 60days 2500
1. Using DataFrame.loc[] to Get a Cell Value by Column Name
In Pandas, DataFrame.loc[] property is used to get a specific cell value by row & lable name(column name). Below all examples return a cell value from row/Index 3 (4th row as index starts from zero) and Duration column (3rd column). In order to refer last column use -1 as column position and for the last row use -1. Though iloc[] is used with integer position for the column, you can also try with column position number
# Using loc[]. Get cell value by name & index
print(df.loc[3]['Duration'])
print(df.loc[3,'Duration'])
print(df.loc[3][2])
Yields below output. Note that on above examples df.loc[3] returns a series.
40days
Note that using loc[] property we are also able to use an index to refer to the column.
2. Using DataFrame.iloc[] to Get a Cell Value by Column Position
If you wanted to get a cell value by column number, DataFrame.iloc[] is the preferred approach. .iloc[] is primarily integer position based starts from 0 to length-1 (index starts from zero). In order to refer last column use -1 as column position. Though iloc[] is used with integer position, you can also try with label name.
# Using iloc[]. Get cell value by index & name
print(df.iloc[3]['Duration'])
print(df.iloc[3][2])
print(df.iloc[3,2])
This returns the same output as above. Note that iloc[] property doesn’t support notation df.iloc[3,'Duration'], using this returns an error.
3. Using DataFrame.at[] to select Specific Cell Value by Column Label Name
DataFrame.at[] property is used to access a single cell by row and column label pair. like loc[] this doesn’t support column by position. This performs better when you wanted to get a specific cell value from Pandas DataFrame as it uses both row and column labels. Note that at[] property doesn’t support negative index to refer rows or columns from last.
# Using DataFrame.at[]
print(df.at[3,'Duration'])
print(df.at[df.index[3],'Duration'])
These examples also yield the same output 40days.
4.Using DataFrame.iat[] select Specific Cell Value by Column Position
DataFrame.iat[] is another property to select a specific cell value by row and column position. Using this you can refer to column only by position but not by a label. This also doesn’t support negative index or column position.
# Using DataFrame.iat[]
print(df.iat[3,2])
5. Select Cell Value from DataFrame Using df[‘col_name’].values[]
We can use df['col_name'].values[] to get 1×1 DataFrame as a NumPy array, then access the first and only value of that array to get a cell value, for instance, df["Duration"].values[3].
#Get a cell value
print(df["Duration"].values[3])
6. Get Cell Value from Last Row of Pandas DataFrame
If you wanted to get a specific cell value from the last Row of Pandas DataFrame, use the negative index to point the rows from last. For example, Index -1 represents the last row and -2 for the second row from the last. Similarly, you should also use -1 for the last column.
# Get cell value from last row
print(df.iloc[-1,2]) # prints 60days
print(df.iloc[-1]['Duration']) # prints 60days
print(df.at[df.index[-1],'Duration']) # prints 60days
# prints 2500
To select the cell value of the last row and last column use df.iloc[-1,-1], this returns 2500. similarly, you can also try other approaches.
Conclusion
In this article, you have learned how to get or select a specific cell value from pandas DataFrame using the .iloc[], .loc[], .iat[] & .at[] properties. Also, you have learned how to get a specific value from the last row and last row, last column with examples.
Happy Learning !!
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