Is there a way I can apply df.describe() to just an isolated column in a DataFrame.
For example if I have several columns and I use df.describe() - it returns and describes all the columns. From research, I understand I can add the following:
"A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'"
However I don't quite know how to write this out in python code. Thanks in advance.
15 Answers
Just add column name in square braquets:
df['column_name'].describe()Example:
To get a single column:
df['1']To get several columns:
df[['1','2']]To get a single row by name:
df.loc['B']or by index:
df.iloc[o]To get a specific field:
df['1']['C']import pandas as pd
data=pd.read_csv('data.csv')
data[['column1', 'column2', 'column3']].describe()import pandas as pd
data = pd.read_csv("ad.data", header=None)
data[111].describe()or for example
lastindice = data[data .columns[-1]]
lastindice.describe()In Pyspark DataFrame you can describe for only one column like this:
df.describe("col1").toPandas()or several columns like this:
df.describe(["col1", "col2"]).toPandas()to describe it as table
df[['column_name']].describe()to describe it as data
df['column_name'].describe()
