python Pandas : Split string into multiple columns and extract data for column from split parameter … here is a solution to the problem.
python Pandas : Split string into multiple columns and extract data for column from split parameter
I’m new to Python and Pandas, there are some URL paths in one column, and I want to split it into separate columns.
Each parameter of the string is separated by a semicolon.
I know there are many other answers on how to split data into multiple columns by delimiters, but in my example I want to dynamically create columns and extract the values from each column from the parameter itself.
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The column in which each parameter should be placed is within the parameter itself, with the data following the equal sign. I want to put the data after the equal sign in the column before the equal sign.
For example:
cat=be_thnky; u1=men
cat=be_thnky; u1=custom
Should become
cat u1
be_thnky men
be_thnky custom
To add complexity, not all parameters are present in every URL, and if the parameters are not, I want the column to contain the NaN.
Some example URL path strings I’m using are:
; src=4457426; type=be_salec; cat=be_thnky; qty=1; cost=60.00;ord=50608803; gtm=G64; gcldc=*; gclaw=*; gac=UA-32723457-1:*; u1=men; u2=schoenen; u3=none; u5=VA38G1NRI; u6=80; u7=0; u8=1; u9=EUR; u10=be; u11=Suede Old Skool Shoes; u12=checkout; u13=8; u14=VNIWTYI926IW7; u15=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=%2B03C782RqELOiuY1L2ELV7hFeTRMquZ9Eyr1lJqmoSQhClENiUJ6feRNwwAA1ZYd4V7tkAuIwyiIrClp7QaqfLeC%2B%2FPTLl7wSF%2FCyrVWqgiSJRgAS%2BWbXohu0DG8xsdPnSXp%2F%2F4MDb% 2FkbPwh%2FT5EpiEWMkGur%2Fx%2FABR7Cvs4jh345776IITNx%2FTRZZXu4zeAco5P%2FvxyqDbmwvLKpPKljf3TpU0wOCmjCDWR5r3uR3ELErPFboWuV5H24FOIy7e% 2B2b6m4YhCCDuzceKa5Qllkiwc4YI6AL9rIK1T2jExde343vk%2B4FZtK6XgOMtxbwv6pBIUMX%2Bn3kbb7soGQ%2FjnEwxzxMX5P%2FdMZzts6NkskMSICB955QKsZqPLepiS%2BWY5u5%2Bs9CPjquK% 2FlsXmHTi26wq1cLqeiPdyolnE2AxaswLDhQcQbvDengszkSu8U8lTDhqaAxLExYF% 2BMstZtKamD14AnMElNAbjZNcTEByzYlXOi1q2FpYg0kCyoaBBBtkRInSDBZtjxNWgd9bl98qs5R2ZqCiHmtOPrfcM53V77Acxcb5wl% 2FkpdKEbTGuAijHpHgxpi55kIEcEmkJjvPnW7RwxUXPiVZbFjh34PlGJ10FaGvqPwsijBpR1TXrKWV3t3Z4r03yViU6txghbNtODiQ%3D%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd; ~oref=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=%2B03C782RqELOiuY1L2ELV7hFeTRMquZ9Eyr1lJqmoSQhClENiUJ6feRNwwAA1ZYd4V7tkAuIwyiIrClp7QaqfLeC%2B%2FPTLl7wSF%2FCyrVWqgiSJRgAS%2BWbXohu0DG8xsdPnSXp%2F%2F4MDb% 2FkbPwh%2FT5EpiEWMkGur%2Fx%2FABR7Cvs4jh345776IITNx%2FTRZZXu4zeAco5P%2FvxyqDbmwvLKpPKljf3TpU0wOCmjCDWR5r3uR3ELErPFboWuV5H24FOIy7e% 2B2b6m4YhCCDuzceKa5Qllkiwc4YI6AL9rIK1T2jExde343vk%2B4FZtK6XgOMtxbwv6pBIUMX%2Bn3kbb7soGQ%2FjnEwxzxMX5P%2FdMZzts6NkskMSICB955QKsZqPLepiS%2BWY5u5%2Bs9CPjquK% 2FlsXmHTi26wq1cLqeiPdyolnE2AxaswLDhQcQbvDengszkSu8U8lTDhqaAxLExYF% 2BMstZtKamD14AnMElNAbjZNcTEByzYlXOi1q2FpYg0kCyoaBBBtkRInSDBZtjxNWgd9bl98qs5R2ZqCiHmtOPrfcM53V77Acxcb5wl% 2FkpdKEbTGuAijHpHgxpi55kIEcEmkJjvPnW7RwxUXPiVZbFjh34PlGJ10FaGvqPwsijBpR1TXrKWV3t3Z4r03yViU6txghbNtODiQ%3D%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd
and
; src=4457426; type=be_salec; cat=be_thnky; qty=1; cost=79.17;ord=50619855; gtm=G64; gac=UA-32723457-1:*; u1=custom; u2=undefined; u3=none; u5=AQNNOQ; u6=95; u7=0; u8=1; u9=EUR; u10=be; u11=Men Era Shoes; u12=checkout; u13=; u14=; u15=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=aaHqAAtJa9bzV4lSFEuMWqdyG11jxs2yT0UY242hWRQyCn%2Ff7AHBrF%2ByFm6GF%2BZiumn%2B6cjIaHASWHpiwsBKSa5k5fMJoyz3ex%2B8FTyDOp3WwLgA9U3ibS6gLNMEl68UQ8K7bVk%2FP1% 2BC2ckY17vriakRKvUpobXypW0AvXHgHGmaleDoIOlM6dVIX1pSHBPbeKDG4JVoXbUOltTgLUcnYbojIiIGx6m%2FYlHnYjWU%2BaYQpCK%2BRBeFd%2FKyekIN9y9wQlZHHKb7pFar8c3S24tuHj%2FeDGe1jwJ0S7% 2BBnUb5WloJ1SSf0LjDyFSZAWBSzhidLIRM2OWyTXJeCBdBFNSw%2BwICm6uWHKPClJD%2FRIzO4D%2F3HQyS4sOeynLgyIR6JHsCv3FH%2B%2BrINsPE0Y3eI51mpm7UEmmcLmNKiONm11LwTD1U% 2FZKgnLe50naDdiYj9%2BCt7TUkNuDiOYq1jaC2yOSKcz%2BGdF2i4bgEttXJlK84ZUeCUhfvGbQNebesaoRLrGgU7FkuOhut3LQm7Lqu5lpKYSt5cV8gkGP5%2Fm%2BOa%2FzKbRNmbcwACXuZ1hBJW0alkcX% 2F3hfpPiSg9UrT1uZKRwfQUpx6fHzagiSWtcWXJDYO2SfWtlfoS%2B7W%2FIvIoD1FtMbCeVC6oAvltLOnIojrW3VYh1OrFUIlXcl0XMXzCPfRz% 2B2v28tFOmsucTRbixJ9WyW3WqN2h3YMHZJQoSFbpUDSN7VQkFJmC1NgHzX09u7X1AUIcwP1TmLqO034RnK6ZSfmS38NuYhWCAmPUIyopyEmxqE3M% 2FzqEWjId6S1DTmaJSzo09Rx2UtLnZXMOLKXifzoN8eQy3yQvFeNsKxh3IkJxb6uifVXDBpyelQibch9gDg%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd; ~oref=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=aaHqAAtJa9bzV4lSFEuMWqdyG11jxs2yT0UY242hWRQyCn%2Ff7AHBrF%2ByFm6GF%2BZiumn%2B6cjIaHASWHpiwsBKSa5k5fMJoyz3ex%2B8FTyDOp3WwLgA9U3ibS6gLNMEl68UQ8K7bVk%2FP1% 2BC2ckY17vriakRKvUpobXypW0AvXHgHGmaleDoIOlM6dVIX1pSHBPbeKDG4JVoXbUOltTgLUcnYbojIiIGx6m%2FYlHnYjWU%2BaYQpCK%2BRBeFd%2FKyekIN9y9wQlZHHKb7pFar8c3S24tuHj%2FeDGe1jwJ0S7% 2BBnUb5WloJ1SSf0LjDyFSZAWBSzhidLIRM2OWyTXJeCBdBFNSw%2BwICm6uWHKPClJD%2FRIzO4D%2F3HQyS4sOeynLgyIR6JHsCv3FH%2B%2BrINsPE0Y3eI51mpm7UEmmcLmNKiONm11LwTD1U% 2FZKgnLe50naDdiYj9%2BCt7TUkNuDiOYq1jaC2yOSKcz%2BGdF2i4bgEttXJlK84ZUeCUhfvGbQNebesaoRLrGgU7FkuOhut3LQm7Lqu5lpKYSt5cV8gkGP5%2Fm%2BOa%2FzKbRNmbcwACXuZ1hBJW0alkcX% 2F3hfpPiSg9UrT1uZKRwfQUpx6fHzagiSWtcWXJDYO2SfWtlfoS%2B7W%2FIvIoD1FtMbCeVC6oAvltLOnIojrW3VYh1OrFUIlXcl0XMXzCPfRz% 2B2v28tFOmsucTRbixJ9WyW3WqN2h3YMHZJQoSFbpUDSN7VQkFJmC1NgHzX09u7X1AUIcwP1TmLqO034RnK6ZSfmS38NuYhWCAmPUIyopyEmxqE3M% 2FzqEWjId6S1DTmaJSzo09Rx2UtLnZXMOLKXifzoN8eQy3yQvFeNsKxh3IkJxb6uifVXDBpyelQibch9gDg%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd
Solution
Here’s a solution that uses dictionary understanding followed by pd.concat:
str1 = '; src=4457426; type=be_salec; cat=be_thnky; qty=1; cost=60.00;ord=50608803; gtm=G64; gcldc=*; gclaw=*; gac=UA-32723457-1:*; u1=men; u2=schoenen; u3=none; u5=VA38G1NRI; u6=80; u7=0; u8=1; u9=EUR; u10=be; u11=Suede Old Skool Shoes; u12=checkout; u13=8; u14=VNIWTYI926IW7; u15=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=%2B03C782RqELOiuY1L2ELV7hFeTRMquZ9Eyr1lJqmoSQhClENiUJ6feRNwwAA1ZYd4V7tkAuIwyiIrClp7QaqfLeC%2B%2FPTLl7wSF%2FCyrVWqgiSJRgAS%2BWbXohu0DG8xsdPnSXp%2F%2F4MDb% 2FkbPwh%2FT5EpiEWMkGur%2Fx%2FABR7Cvs4jh345776IITNx%2FTRZZXu4zeAco5P%2FvxyqDbmwvLKpPKljf3TpU0wOCmjCDWR5r3uR3ELErPFboWuV5H24FOIy7e% 2B2b6m4YhCCDuzceKa5Qllkiwc4YI6AL9rIK1T2jExde343vk%2B4FZtK6XgOMtxbwv6pBIUMX%2Bn3kbb7soGQ%2FjnEwxzxMX5P%2FdMZzts6NkskMSICB955QKsZqPLepiS%2BWY5u5%2Bs9CPjquK% 2FlsXmHTi26wq1cLqeiPdyolnE2AxaswLDhQcQbvDengszkSu8U8lTDhqaAxLExYF% 2BMstZtKamD14AnMElNAbjZNcTEByzYlXOi1q2FpYg0kCyoaBBBtkRInSDBZtjxNWgd9bl98qs5R2ZqCiHmtOPrfcM53V77Acxcb5wl% 2FkpdKEbTGuAijHpHgxpi55kIEcEmkJjvPnW7RwxUXPiVZbFjh34PlGJ10FaGvqPwsijBpR1TXrKWV3t3Z4r03yViU6txghbNtODiQ%3D%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd; ~oref=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=%2B03C782RqELOiuY1L2ELV7hFeTRMquZ9Eyr1lJqmoSQhClENiUJ6feRNwwAA1ZYd4V7tkAuIwyiIrClp7QaqfLeC%2B%2FPTLl7wSF%2FCyrVWqgiSJRgAS%2BWbXohu0DG8xsdPnSXp%2F%2F4MDb% 2FkbPwh%2FT5EpiEWMkGur%2Fx%2FABR7Cvs4jh345776IITNx%2FTRZZXu4zeAco5P%2FvxyqDbmwvLKpPKljf3TpU0wOCmjCDWR5r3uR3ELErPFboWuV5H24FOIy7e% 2B2b6m4YhCCDuzceKa5Qllkiwc4YI6AL9rIK1T2jExde343vk%2B4FZtK6XgOMtxbwv6pBIUMX%2Bn3kbb7soGQ%2FjnEwxzxMX5P%2FdMZzts6NkskMSICB955QKsZqPLepiS%2BWY5u5%2Bs9CPjquK% 2FlsXmHTi26wq1cLqeiPdyolnE2AxaswLDhQcQbvDengszkSu8U8lTDhqaAxLExYF% 2BMstZtKamD14AnMElNAbjZNcTEByzYlXOi1q2FpYg0kCyoaBBBtkRInSDBZtjxNWgd9bl98qs5R2ZqCiHmtOPrfcM53V77Acxcb5wl% 2FkpdKEbTGuAijHpHgxpi55kIEcEmkJjvPnW7RwxUXPiVZbFjh34PlGJ10FaGvqPwsijBpR1TXrKWV3t3Z4r03yViU6txghbNtODiQ%3D%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd'
str2 = '; src=4457426; type=be_salec; cat=be_thnky; qty=1; cost=79.17;ord=50619855; gtm=G64; gac=UA-32723457-1:*; u1=custom; u2=undefined; u3=none; u5=AQNNOQ; u6=95; u7=0; u8=1; u9=EUR; u10=be; u11=Men Era Shoes; u12=checkout; u13=; u14=; u15=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=aaHqAAtJa9bzV4lSFEuMWqdyG11jxs2yT0UY242hWRQyCn%2Ff7AHBrF%2ByFm6GF%2BZiumn%2B6cjIaHASWHpiwsBKSa5k5fMJoyz3ex%2B8FTyDOp3WwLgA9U3ibS6gLNMEl68UQ8K7bVk%2FP1% 2BC2ckY17vriakRKvUpobXypW0AvXHgHGmaleDoIOlM6dVIX1pSHBPbeKDG4JVoXbUOltTgLUcnYbojIiIGx6m%2FYlHnYjWU%2BaYQpCK%2BRBeFd%2FKyekIN9y9wQlZHHKb7pFar8c3S24tuHj%2FeDGe1jwJ0S7% 2BBnUb5WloJ1SSf0LjDyFSZAWBSzhidLIRM2OWyTXJeCBdBFNSw%2BwICm6uWHKPClJD%2FRIzO4D%2F3HQyS4sOeynLgyIR6JHsCv3FH%2B%2BrINsPE0Y3eI51mpm7UEmmcLmNKiONm11LwTD1U% 2FZKgnLe50naDdiYj9%2BCt7TUkNuDiOYq1jaC2yOSKcz%2BGdF2i4bgEttXJlK84ZUeCUhfvGbQNebesaoRLrGgU7FkuOhut3LQm7Lqu5lpKYSt5cV8gkGP5%2Fm%2BOa%2FzKbRNmbcwACXuZ1hBJW0alkcX% 2F3hfpPiSg9UrT1uZKRwfQUpx6fHzagiSWtcWXJDYO2SfWtlfoS%2B7W%2FIvIoD1FtMbCeVC6oAvltLOnIojrW3VYh1OrFUIlXcl0XMXzCPfRz% 2B2v28tFOmsucTRbixJ9WyW3WqN2h3YMHZJQoSFbpUDSN7VQkFJmC1NgHzX09u7X1AUIcwP1TmLqO034RnK6ZSfmS38NuYhWCAmPUIyopyEmxqE3M% 2FzqEWjId6S1DTmaJSzo09Rx2UtLnZXMOLKXifzoN8eQy3yQvFeNsKxh3IkJxb6uifVXDBpyelQibch9gDg%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd; ~oref=https://www.vans.be/webapp/wcs/stores/servlet/OrderOKView?langId=-27&catalogId=11260&storeId=10167& krypto=aaHqAAtJa9bzV4lSFEuMWqdyG11jxs2yT0UY242hWRQyCn%2Ff7AHBrF%2ByFm6GF%2BZiumn%2B6cjIaHASWHpiwsBKSa5k5fMJoyz3ex%2B8FTyDOp3WwLgA9U3ibS6gLNMEl68UQ8K7bVk%2FP1% 2BC2ckY17vriakRKvUpobXypW0AvXHgHGmaleDoIOlM6dVIX1pSHBPbeKDG4JVoXbUOltTgLUcnYbojIiIGx6m%2FYlHnYjWU%2BaYQpCK%2BRBeFd%2FKyekIN9y9wQlZHHKb7pFar8c3S24tuHj%2FeDGe1jwJ0S7% 2BBnUb5WloJ1SSf0LjDyFSZAWBSzhidLIRM2OWyTXJeCBdBFNSw%2BwICm6uWHKPClJD%2FRIzO4D%2F3HQyS4sOeynLgyIR6JHsCv3FH%2B%2BrINsPE0Y3eI51mpm7UEmmcLmNKiONm11LwTD1U% 2FZKgnLe50naDdiYj9%2BCt7TUkNuDiOYq1jaC2yOSKcz%2BGdF2i4bgEttXJlK84ZUeCUhfvGbQNebesaoRLrGgU7FkuOhut3LQm7Lqu5lpKYSt5cV8gkGP5%2Fm%2BOa%2FzKbRNmbcwACXuZ1hBJW0alkcX% 2F3hfpPiSg9UrT1uZKRwfQUpx6fHzagiSWtcWXJDYO2SfWtlfoS%2B7W%2FIvIoD1FtMbCeVC6oAvltLOnIojrW3VYh1OrFUIlXcl0XMXzCPfRz% 2B2v28tFOmsucTRbixJ9WyW3WqN2h3YMHZJQoSFbpUDSN7VQkFJmC1NgHzX09u7X1AUIcwP1TmLqO034RnK6ZSfmS38NuYhWCAmPUIyopyEmxqE3M% 2FzqEWjId6S1DTmaJSzo09Rx2UtLnZXMOLKXifzoN8eQy3yQvFeNsKxh3IkJxb6uifVXDBpyelQibch9gDg%3D&ddkey=https%3AVFCWorldpayPunchoutCallbackCmd'
def converter(x):
return dict(i.split('=', 1) for i in str1.split('; ') if '=' in i)
res = pd.concat([pd. DataFrame.from_dict(converter(i), orient='index'). T \
for i in (str1, str2)])
Result:
print(res)
src type cat qty cost ord gtm gcldc gclaw \
0 4457426 be_salec be_thnky 1 60.00 50608803 G64 * *
0 4457426 be_salec be_thnky 1 60.00 50608803 G64 * *
~oref
0 https://www.vans.be/webapp/wcs/stores/servlet/...
0 https://www.vans.be/webapp/wcs/stores/servlet/...
[2 rows x 25 columns]