Pandas：将值从一列转移到另一列，并使用 python 删除重复项

Question

Suppose I am getting a dataframe like this:

Name            value
Umicore         470
889 
19  
912 
1.68    
    
Shopify         19
500 
17  
51  
1.44    

How do get a dataframe such that I will be left with this output

Name            value
Umicore         1.68    
Shopify         1.44    

This is how I am getting my dataframe:

#my_df['Name'].replace('', np.nan, inplace=True)
#my_df['Name'].replace('', np.nan).ffill(inplace=True) #tried just now fails
#my_df['value'].replace('', np.nan, inplace=True)
#my_df.dropna(subset=['Name', 'value'], inplace=True)
my_df.drop_duplicates(keep='last', inplace=True)
my_df.to_csv('output.csv', index=False)

How do I shift the numbers from Name column to Value column? Please help!

Answer 1

Use:

#if empty strings or NaNs in Name column remove these rows
df['Name'] = df['Name'].replace('', np.nan)
df = df.dropna(subset=['Name'])

#create default index
df = df.reset_index(drop=True)    

print (df)
      Name  value
0  Umicore  470.0
1      889    NaN
2       19    NaN
3      912    NaN
4     1.68    NaN
5  Shopify   19.0
6      500    NaN
7       17    NaN
8       51    NaN
9     1.44    NaN

---

#convert values to numeric, if no numeric is NaN created
s = pd.to_numeric(df['Name'], errors='coerce')
#forward filling missing values by non numeric values
df['Name'] = df['Name'].where(s.isna()).ffill()
#set value by s
df['value'] = s

print (df)
      Name   value
0  Umicore     NaN
1  Umicore  889.00
2  Umicore   19.00
3  Umicore  912.00
4  Umicore    1.68 <- last value of Umicore
5  Shopify     NaN
6  Shopify  500.00
7  Shopify   17.00
8  Shopify   51.00
9  Shopify    1.44

#remove duplicates by Name column
df = df.drop_duplicates('Name',keep='last')
print (df)
      Name  value
4  Umicore   1.68
9  Shopify   1.44