pandas : Lambda ,Map and Eval Conditions

Lambda ,Map and eval in Pandas

1. Simple 

import pandas as pd

a=pd.DataFrame({"a":["deea","123","deep","deepak"]})

b=pd.DataFrame({"b":["deeap","1234","deepak","deepa"]})

c=pd.DataFrame(map(lambda x,y:x in y,a['a'],b['b']))

print(c)

 

 

result : 

       0

0   True

1   True

2   True

3  False 

 

2. Using Dynamic Formula :

import pandas as pd

a=pd.DataFrame({"a":["deea","123","deep","deepak"]})

b=pd.DataFrame({"b":["deeap","1234","deepak","deepa"]})

c=pd.DataFrame(map(eval(input("enter -lambda x,y:x in y")),a['a'].values,b['b'].values))

print(c)

       0

0   True

1   True

2   True

3  False

 

3)Using Dynamic Formula and 1st columns

import pandas as pd

a=pd.DataFrame({"a":["deea","123","deep","deepak"]})

b=pd.DataFrame({"b":["deeap","1234","deepak","deepa"]})

c=pd.DataFrame(map(eval(input("enter -lambda x,y:x in y")),a.iloc[:,0],b.iloc[:,0]))

print(c)

  

Result: 

  0

0 True

1 True

2 True

3 False 

 

"""

"""

Column Names

data.iloc[:,0] # first column of data frame (first_name)
data.iloc[:,1] # second column of data frame (last_name)
data.iloc[:,-1] # last column of data frame (id)

Rows and Columns
data.iloc[0:5] # first five rows of dataframe
data.iloc[:, 0:2] # first two columns of data frame with all rows
data.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns.
data.iloc[0:5, 5:8] # first 5 rows and 5th, 6th, 7th columns of data frame
"""

Numpy : Lambda / map / vectorize

Numpy : Lambda / map / vectorize

import numpy as np

def myfunc(a, b):

    if a > b:

        return a - b

    else:

        return a + b

n1=np.array([1,2,3])

n2=np.array([5,4,3])

vfunc = np.vectorize(myfunc)

print(vfunc(n1,n2))

Output :

[6 6 6]

pandas : Import snippet

pandas : Import snippet

TC_ID	TC_NAME	QUERY1	QUERY2	CONDITION	BASICTEST(QUERY1/QUERY2)
TC0001	TC_NAME1	Select col1 from table1	Select col1 from table2	1	11
TC0002	TC_NAME2	Select col1 from table2	Select col1 from table3	2	12
TC0003	TC_NAME3	Select col1 from table3	Select col1 from table4	3	13
TC0004	TC_NAME4	Select col1 from table4	Select col1 from table5	4	14
TC0005	TC_NAME5	Select col1 from table5	Select col1 from table6	5	15
TC0006	TC_NAME6	Select col1 from table6	Select col1 from table7	6	16

To select row and Traverse cells from selected rows 
import pandas as pd 
df=pd.read_csv("/home/deepak/PycharmProjects/csv_test/test_cases.csv")
O_row=(df[df["TC_ID"]=="TC0006"])
print(O_row["TC_NAME"].values[0])
 
Output:
TC_NAME6 

To convert CSV to list of dictionaries:
 (Using Pandas)
import pandas as pd
import csv
df = pd.read_csv(sPath)
list_of_data = df.to_dict(orient='records')#dropna().to_dict(orient='recorddel df
return list_of_data
 
(Using CSV)
import pandas as pd
import csv 
with open(sPath, "r") as f:
    reader = csv.DictReader(f)
    list_dict = list(reader)
    return list_dict