Add new column to a pandas dataframe based on conditional statement of existing column

Question

Ihave a DataFrame, looks like this:

    Date            Price
0   Jan 04, 1999    1.1827
1   Jan 05, 1999    1.1764
2   Jan 06, 1999    1.1618
3   Jan 07, 1999    1.1709
4   Jan 08, 1999    1.1575
5   Jan 11, 1999    1.1502
6   Jan 12, 1999    1.1572
7   Jan 13, 1999    1.1673
8   Jan 14, 1999    1.1698
9   Jan 15, 1999    1.1557

I would like to add a new column, which counted in Excel like this: =IF((B2-B1)>0;B2-B1;0), so it should look like this:

    Date            Price   up
0   Jan 04, 1999    1.1827  0.0000
1   Jan 05, 1999    1.1764  0.0000
2   Jan 06, 1999    1.1618  0.0000
3   Jan 07, 1999    1.1709  0.0091
4   Jan 08, 1999    1.1575  0.0000
5   Jan 11, 1999    1.1502  0.0000
6   Jan 12, 1999    1.1572  0.0070
7   Jan 13, 1999    1.1673  0.0101
8   Jan 14, 1999    1.1698  0.0025
9   Jan 15, 1999    1.1557  0.0000

Actually, I have a solution, but I am not satisfied with it:

ser=[]
i = 0
while i < len(df["Price"]):
    if i==0:
        ser.append(0)
    elif df["Price"][i]-df["Price"][i-1]>0:
        ser.append((df["Price"][i]-df["Price"][i-1]))
    else:
        ser.append(0)
    i = i+1
df["up"]=ser

Is there any more elegant solution? Thanks!

jezrael · Accepted Answer · 2017-11-02 09:26:00Z

2

Use diff with where:

dif = df['Price'].diff()
df['up'] = dif.where(dif > 0, 0)
#alternative
#df['up'] = np.where(dif > 0, dif, 0)
print (df)
           Date   Price      up
0  Jan 04, 1999  1.1827  0.0000
1  Jan 05, 1999  1.1764  0.0000
2  Jan 06, 1999  1.1618  0.0000
3  Jan 07, 1999  1.1709  0.0091
4  Jan 08, 1999  1.1575  0.0000
5  Jan 11, 1999  1.1502  0.0000
6  Jan 12, 1999  1.1572  0.0070
7  Jan 13, 1999  1.1673  0.0101
8  Jan 14, 1999  1.1698  0.0025
9  Jan 15, 1999  1.1557  0.0000

answered Nov 2, 2017 at 9:26

jezrael

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