pandas conditional selection code example

Example 1: new dataframe based on certain row conditions

filterinfDataframe = dfObj[(dfObj['Sale'] > 30) & (dfObj['Sale'] < 33) ]

Example 2: slice dataframe pandas based on condition

# To slice pandas dataframe by condition

frioMurteira = data.loc[(data["POM"] == "Murteira") & (data["TMP"] > 7.2), ["DTM","TMP"]]

Example 3: new dataframe based on certain row conditions

# Create variable with TRUE if nationality is USA
american = df['nationality'] == "USA"

# Create variable with TRUE if age is greater than 50
elderly = df['age'] > 50

# Select all cases where nationality is USA and age is greater than 50
df[american & elderly]

Example 4: python pandas dataframe conditional subset

In [722]: df[(df['C']==1) | df['B']]
Out[722]:
   A      B  C
0  1   True  1
2  2  False  1
3  2   True  2
4  3   True  1

In [723]: df.query('C==1 or B==True')
Out[723]:
   A      B  C
0  1   True  1
2  2  False  1
3  2   True  2
4  3   True  1

In [724]: df[df.eval('C==1 or B==True')]
Out[724]:
   A      B  C
0  1   True  1
2  2  False  1
3  2   True  2
4  3   True  1

Example 5: conditional value if else pandas np.select

# create a list of our conditions
conditions = [
    (df['likes_count'] <= 2),
    (df['likes_count'] > 2) & (df['likes_count'] <= 9),
    (df['likes_count'] > 9) & (df['likes_count'] <= 15),
    (df['likes_count'] > 15)
    ]

# create a list of the values we want to assign for each condition
values = ['tier_4', 'tier_3', 'tier_2', 'tier_1']

# create a new column and use np.select to assign values to it using our lists as arguments
df['tier'] = np.select(conditions, values)

# display updated DataFrame
df.head()

Example 6: pandas specific row in column

In [25]: titanic.iloc[9:25, 2:5]
Out[25]: 
    Pclass                                 Name     Sex
9        2  Nasser, Mrs. Nicholas (Adele Achem)  female
10       3      Sandstrom, Miss. Marguerite Rut  female
11       1             Bonnell, Miss. Elizabeth  female
12       3       Saundercock, Mr. William Henry    male
13       3          Andersson, Mr. Anders Johan    male
..     ...                                  ...     ...
20       2                 Fynney, Mr. Joseph J    male
21       2                Beesley, Mr. Lawrence    male
22       3          McGowan, Miss. Anna "Annie"  female
23       1         Sloper, Mr. William Thompson    male
24       3        Palsson, Miss. Torborg Danira  female

[16 rows x 3 columns]