So I have a dataframe like this, and I want to find how many students have more than two school experiences.

df["Name","Primary school","Middle school","High School"].isnull()

We can use df.count_value() to get the column summary, but how could I get sum value of rows?

desired output

df["Enough Experience?"]= #code

CodePudding user response：

Based on your description of the problem, I think you need:

df['Enough experience?'] = df.drop(columns='Name').sum(axis=1).ge(2)

You shouldn't use isnull, that checks for NaN/None, instead sum the booleans (each True counts for 1).

output:

   Name  Primary school  Middle school  High School  Enough experience?
0  Alex           False          False        False               False
1  Peng           False          False         True               False
2    Hu           False          False         True               False

To get the total:

experienced_students = df.drop(columns='Name').sum(axis=1).ge(2).sum()

output: 2

Count of schools:

df['number schools'] = df.drop(columns='Name').sum(axis=1)

   Name  Primary school  Middle school  High School  number schools
0  Alex           False          False        False               0
1  Peng           False          False         True               1
2    Hu           False          False         True               1

CodePudding user response：

Use:

print (df)
   Name  Primary school  Middle school  High School
0  Alex             1.0            NaN          NaN
1  Peng             1.0            NaN          2.0
2    Hu             2.0            7.0          0.0
3  John             NaN            NaN          NaN


# test if all non missing values 
cols = ["Primary school","Middle school","High School"]
df["Enough Experience?1"] = ~df[cols].isnull().any(axis=1)
# test at least 1 missing values 
df["Enough Experience?2"] = df[cols].isnull().any(axis=1)

# test at least 2 missing values per rows with `sum`
df["Enough Experience?3"] = df[cols].isnull().sum(axis=1).lt(2)
df["Enough Experience?4"] = df[cols].count(axis=1).ge(2)
print (df)
   Name  Primary school  Middle school  High School  Enough Experience?1  \
0  Alex             1.0            NaN          NaN                False   
1  Peng             1.0            NaN          2.0                False   
2    Hu             2.0            7.0          0.0                 True   
3  John             NaN            NaN          NaN                False   

   Enough Experience?2  Enough Experience?3  Enough Experience?4  
0                 True                False                False  
1                 True                 True                 True  
2                False                 True                 True  
3                 True                False                False  

Solutions by table ouput - testing if all values are not missing:

# test at least 1 missing values 
cols = ["Primary school","Middle school","High School"]
df["Enough Experience?1"] = df[cols].notna().all(axis=1)
df["Enough Experience?2"] = ~df[cols].isna().any(axis=1)
df["Enough Experience?3"] = df[cols].isnull().sum(axis=1)<1
print (df)
   Name  Primary school  Middle school  High School  Enough Experience?1  \
0  Alex             1.0            NaN          NaN                False   
1  Peng             1.0            NaN          2.0                False   
2    Hu             2.0            7.0          0.0                 True   
3  John             NaN            NaN          NaN                False   

   Enough Experience?2  Enough Experience?3  
0                False                False  
1                False                False  
2                 True                 True  
3                False                False