{"id":111453,"date":"2020-04-12T15:50:41","date_gmt":"2020-04-12T15:50:41","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=7512"},"modified":"2020-04-12T15:50:41","modified_gmt":"2020-04-12T15:50:41","slug":"how-to-calculate-the-column-standard-deviation-of-a-dataframe-in-python-pandas","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2020\/04\/12\/how-to-calculate-the-column-standard-deviation-of-a-dataframe-in-python-pandas\/","title":{"rendered":"How to Calculate the Column Standard Deviation of a DataFrame in Python Pandas?"},"content":{"rendered":"

Want to calculate the standard deviation of a column in your Pandas <\/a>DataFrame?<\/p>\n

In case you’ve attended your last statistics course a few years ago, let’s quickly recap the definition of variance<\/strong>: it’s the average squared deviation of the list elements from the average value.<\/em><\/p>\n

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You can do this by using the pd.std()<\/code> function that calculates the standard deviation along all columns. You can then get the column you’re interested in after the computation.<\/strong><\/p>\n

import pandas as pd # Create your Pandas DataFrame\nd = {'username': ['Alice', 'Bob', 'Carl'], 'age': [18, 22, 43], 'income': [100000, 98000, 111000]}\ndf = pd.DataFrame(d) print(df)<\/pre>\n
Your DataFrame looks like this:<\/p>\n
\n\n\n\n\n\n\n
<\/td>\nusername<\/td>\nage<\/td>\nincome<\/td>\n<\/tr>\n
0<\/td>\nAlice<\/td>\n18<\/td>\n100000<\/td>\n<\/tr>\n
1<\/td>\nBob<\/td>\n22<\/td>\n98000<\/td>\n<\/tr>\n
2<\/td>\nCarl<\/td>\n43<\/td>\n111000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n
Here’s how you can calculate the standard deviation of all columns:<\/p>\n
print(df.std())<\/pre>\n
The output is the standard deviation of all columns:<\/p>\n
age 13.428825\nincome 7000.000000\ndtype: float64<\/pre>\n
To get the variance of an individual column, access it using simple indexing:<\/p>\n
print(df.std()['age'])\n# 180.33333333333334<\/pre>\n
Together, the code looks as follows. Use the interactive shell to play with it!<\/p>\n