Shulou(Shulou.com)06/01 Report--

This article mainly introduces the solution to the missing value of NaN in pandas, which has a certain reference value, and interested friends can refer to it. I hope you will gain a lot after reading this article.

This paper mainly introduces the methods to deal with the missing values of NaN in pandas, there are two main methods, the details are as follows:

Import pandas as pd missing value processing

There are two ways:

Delete samples with missing values

Replacement / interpolation

The processing missing value is NaN

To determine whether NaN exists in the data, use either of the following two methods

Pd.isnull (dataframe) # dataframe is the data if NaN returns True in the data, if not, return Falsepd.notnull (dataframe) this method is the opposite of isnull any () and all () "" pd.isnull (dataframe). Any () determines which field has missing values and returns Falsepd.notnull (dataframe). All () determines which field has missing values and returns True ""

Numpy can also be used to judge.

Import numpy as npnp.any (pd.isnull (dataframe)) # if True is returned, there is a missing value np.all (pd.notnull (dataframe)) # if False is returned, there is a missing value in the data

Then carry on the data processing.

Method 1: delete null rows

Dataframe.dropna (inplace=False) "dropna () is the method to delete null data. By default, delete the entire row of data containing NaN. If you want to delete the whole row of data with empty values, you need to add the how='all' parameter by default. If you delete the column, you need to add the axis parameter. Axis=1 means to delete the column, and axis=0 indicates whether to delete the row inplace: whether to do this in the current dataframe. True means to modify based on the original, and False means to return a new value without modifying the original data "".

Method 2: replacement / interpolation

Dataframe.fillna ('replacement value value',inplace=False)' pass the value of replacement NaN to fillna ()''missing value NaN has a default tag value

For example, some null values are not NaN, some are a'?'

Replace first

Use numpy to put "?" Replace with NaN

Import numpy as np# replace dataframe.replace (to_replace= "?", value=np.nan)

Change the other missing values to NaN, and then operate according to the missing value of NaN.

Delete data

If you only delete data individually, you can use the drop () method.

DataFrame.drop (labels=None,axis=0, index=None, columns=None, inplace=False)''Code explanation: labels: the name of the column to be deleted. Specify index with the list: directly specify the row to be deleted columns: directly specify the column to be deleted inplace=False: return a new value without modifying the original data inplace=True: modify' 'based on the original

Example:

Import pandas as pddf = pd.read_csv ('/ text.xlsx') # Delete line 0 and line 1 df.drop (labels= [0 1], axis=0) # Delete the column named age df.drop (axis=1,columns=age) Thank you for reading this article carefully. I hope the article "the solution to the missing value of NaN in pandas" shared by the editor will be helpful to you. At the same time, I hope you will support me and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.