Rohan Bapat
Reputation: 352
Python 3.x - Create dataframe and specify column names
I am new to pandas. I have to create a data frame where one column is 'Source' and second column is 'Amount'.
Created a new data frame
df=[]
Now how can i add a columns 'Source' and 'Amount' to this dataframe. The end result is
print(df)
Source Amount
S1 10
S2 12
S3 8
S4 5
The data will come from a for loop. each iteration generates the source and then amount. I want to create a data frame like -
df=[]
for str in some_variable:
df['Source'].append(str[0]) #str[0] will contain source elements
df['Amount'].append(str[1]) #str[1] will contain amount elements
Code -
import requests
import pandas as pd
import bs4
import string
import matplotlib.pyplot as plt
url = "https://www.revisor.mn.gov/laws/?year=2014&type=0&doctype=Chapter&id=294"
result = requests.get(url)
soup = bs4.BeautifulSoup(result.content)
summary = soup.find("div", {"class":"bill_section","id": "laws.1.1.0"})
tables = summary.findAll('table')
data_table = tables[1]
df=pd.DataFrame(['Source','Amount']) #Trying to incorrectly add columns to df
for row in data_table.findAll("tr"):
cells = row.findAll("td")
try :
for char in cells[0].findAll("ins"):
df['Source'] = df['Source'].append() #This is where the issue is
for char in cells[2].findAll("ins"):
df['Amount'] = df['Amount'].append() #And here
except:
pass
Upvotes: 1
Views: 2032
Answers (1)
jezrael
Reputation: 863631
I think you can use read_html with replace, str.strip, str.replace and last to_numeric:
import pandas as pd
import matplotlib.pyplot as plt
url = "https://www.revisor.mn.gov/laws/?year=2014&type=0&doctype=Chapter&id=294"
#read second table in url
df = pd.read_html(url)[1]
#replace texts to empty string
df = df.replace('new text begin','', regex=True).replace('new text end','', regex=True)
#set new columns names
df.columns = ['Source','b','Amount']
#remove first row
df = df[1:]
#remove second column b
df = df.drop('b', axis=1)
#strip whitespaces
df.Source = df.Source.str.strip()
#strip whitespaces and remove (),
df.Amount = df.Amount.str.strip().str.replace(r'[(),]','')
#convert column Amount to numeric
df.Amount = pd.to_numeric(df.Amount)
#reset index
df = df.reset_index(drop=True)
print df
Source Amount
0 University of Minnesota 119367000
1 Minnesota State Colleges and Universities 159812000
2 Education 7491000
3 Minnesota State Academies 11354000
4 Perpich Center for Arts Education 2000000
5 Natural Resources 63480000
6 Pollution Control Agency 2625000
7 Board of Water and Soil Resources 8000000
8 Agriculture 203000
9 Zoological Garden 12000000
10 Administration 127000000
11 Minnesota Amateur Sports Commission 7973000
12 Military Affairs 3244000
13 Public Safety 4030000
14 Transportation 57263000
15 Metropolitan Council 45968000
16 Human Services 86387000
17 Veterans Affairs 2800000
18 Corrections 11881000
19 Employment and Economic Development 92130000
20 Public Facilities Authority 45993000
21 Housing Finance Agency 20000000
22 Minnesota Historical Society 12002000
23 Bond Sale Expenses 900000
24 Cancellations 10849000
25 TOTAL 893054000
26 Bond Proceeds Fund (General Fund Debt Service) 814745000
27 Bond Proceeds Fund (User Financed Debt Service) 39104000
28 State Transportation Fund 36613000
29 Maximum Effort School Loan Fund 5491000
30 Trunk Highway Fund 7950000
31 Bond Proceeds Cancellations 10849000
print df.dtypes
Source object
Amount int64
dtype: object
But if you need solution with parsing data with BeautifulSoup, first Source and Amount are appended by data and then is created DataFrame:
import requests
import pandas as pd
import bs4
import string
import matplotlib.pyplot as plt
url = "https://www.revisor.mn.gov/laws/?year=2014&type=0&doctype=Chapter&id=294"
result = requests.get(url)
soup = bs4.BeautifulSoup(result.content)
summary = soup.find("div", {"class":"bill_section","id": "laws.1.1.0"})
tables = summary.findAll('table')
data_table = tables[1]
Source, Amount = [], []
for row in data_table.findAll("tr"):
cells = row.findAll("td")
try :
for char in cells[0].findAll("ins"):
Source.append(char.text) #This is where the issue is
for char in cells[2].findAll("ins"):
Amount.append(char.text) #And here
except:
pass
print Source
[u'SUMMARY', u'University of Minnesota', u'Minnesota State Colleges and Universities', u'Education', u'Minnesota State Academies', u'Perpich Center for Arts Education', u'Natural Resources', u'Pollution Control Agency', u'Board of Water and Soil Resources', u'Agriculture', u'Zoological Garden', u'Administration', u'Minnesota Amateur Sports Commission', u'Military Affairs', u'Public Safety', u'Transportation', u'Metropolitan Council', u'Human Services', u'Veterans Affairs', u'Corrections', u'Employment and Economic Development', u'Public Facilities Authority', u'Housing Finance Agency', u'Minnesota Historical Society', u'Bond Sale Expenses', u'Cancellations', u'TOTAL', u'Bond Proceeds Fund (General Fund Debt Service)', u'Bond Proceeds Fund (User Financed Debt Service)', u'State Transportation Fund', u'Maximum Effort School Loan Fund', u'Trunk Highway Fund', u'Bond Proceeds Cancellations']
print Amount
[u'119,367,000', u'159,812,000', u'7,491,000', u'11,354,000', u'2,000,000', u'63,480,000', u'2,625,000', u'8,000,000', u'203,000', u'12,000,000', u'127,000,000', u'7,973,000', u'3,244,000', u'4,030,000', u'57,263,000', u'45,968,000', u'86,387,000', u'2,800,000', u'11,881,000', u'92,130,000', u'45,993,000', u'20,000,000', u'12,002,000', u'900,000', u'(10,849,000)', u'893,054,000', u'814,745,000', u'39,104,000', u'36,613,000', u'5,491,000', u'7,950,000', u'(10,849,000)']
print len(Source)
#33
print len(Amount)
#32
#remove first element
Source = Source[1:]
df=pd.DataFrame({'Source':Source,'Amount':Amount}, columns=['Source','Amount'])
print df
Source Amount
0 University of Minnesota 119,367,000
1 Minnesota State Colleges and Universities 159,812,000
2 Education 7,491,000
3 Minnesota State Academies 11,354,000
4 Perpich Center for Arts Education 2,000,000
5 Natural Resources 63,480,000
6 Pollution Control Agency 2,625,000
7 Board of Water and Soil Resources 8,000,000
8 Agriculture 203,000
9 Zoological Garden 12,000,000
10 Administration 127,000,000
11 Minnesota Amateur Sports Commission 7,973,000
12 Military Affairs 3,244,000
13 Public Safety 4,030,000
14 Transportation 57,263,000
15 Metropolitan Council 45,968,000
16 Human Services 86,387,000
17 Veterans Affairs 2,800,000
18 Corrections 11,881,000
19 Employment and Economic Development 92,130,000
20 Public Facilities Authority 45,993,000
21 Housing Finance Agency 20,000,000
22 Minnesota Historical Society 12,002,000
23 Bond Sale Expenses 900,000
24 Cancellations (10,849,000)
25 TOTAL 893,054,000
26 Bond Proceeds Fund (General Fund Debt Service) 814,745,000
27 Bond Proceeds Fund (User Financed Debt Service) 39,104,000
28 State Transportation Fund 36,613,000
29 Maximum Effort School Loan Fund 5,491,000
30 Trunk Highway Fund 7,950,000
31 Bond Proceeds Cancellations (10,849,000)
Upvotes: 2
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