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Trending Discussions on Consumer Goods
Pandas DataFrame if it doesn't contain certain substrings
Repeat Row Values
how to break up data in column value to multiple rows in pandas dataframe
Python Sorting through nested dictionary
R: How can I lower the amount of levels in a dataframe?
R: How can I make a column with lots of categorical levels binary?
QUESTION
Pandas DataFrame if it doesn't contain certain substrings
Asked 2021-Dec-14 at 22:00I'm creating a program to get an overview of my expenses by category. However, I cannot possibly write down every place where I use my card, so at the end I want to categorize all transactions that do not have my set category names in the 'Category' column already to be categorized as "Other".
Below is how I try to do it, searched for solutions and some people just told to whip a ~ in front of the action to make it do the opposite. Like regular negation. Ain't working. What's the optimal solution here?
My idea here is that wherever the Category isn't Entertainment, Microinvestments etc, the Category column cell in that row will be set to "Other".
1df['Category'] = np.where(~df['Category'].str.contains('Entertainment|Microinvestments|Food|Transport|Transfers|Cash|Bills|Apparel|Consumer goods|Services', case=False), 'Other', df['Category'])
2ANSWER
Answered 2021-Dec-14 at 22:00Try this:
1df['Category'] = np.where(~df['Category'].str.contains('Entertainment|Microinvestments|Food|Transport|Transfers|Cash|Bills|Apparel|Consumer goods|Services', case=False), 'Other', df['Category'])
2df.loc[~df['Category'].str.contains('Entertainment|Microinvestments|Food|Transport|Transfers|Cash|Bills|Apparel|Consumer goods|Services', case=False), 'Category'] = 'Other'
3QUESTION
Repeat Row Values
Asked 2021-Nov-12 at 12:59I have the following data:
1with source (Account,AccountNumber,Indentation) as
2(
3select 'INCOME STATEMENT',1000,0 union all
4select 'REVENUE',1100,0 union all
5select 'Revenue - Aircon',1110,1 union all
6select 'Revenue - Consumer Goods',1120,1 union all
7select 'Revenue - Spares',1130,1 union all
8select 'Revenue - Accessories',1140,1 union all
9select 'Revenue - Sub Stock',1150,1 union all
10select 'Revenue - Services',1160,1 union all
11select 'Revenue - Other',1170,1 union all
12select 'Revenue - Intercompany',1180,1 union all
13select 'Revenue - Delivery Charges',1400,1 union all
14select 'COST OF SALES',1500,0 union all
15select 'COGS - Aircon',1510,1 union all
16select 'COGS - Consumer Goods',1520,1 union all
17select 'COGS - Spares',1530,1 union all
18select 'COGS - Accessories',1540,1 union all
19select 'COGS - Sub Stock',1550,1 union all
20select 'COGS - Services',1560,1 union all
21select 'COGS - Other',1570,1 union all
22select 'COGS - Intercompany',1580,1 union all
23select 'COS - Sub Stock Stock Adjustments',1610,1 union all
24select 'COS - Sub Stock Repairs',1620,1 union all
25select 'COS - Consumables & Packing Materials',1810,1 union all
26select 'COS - Freight & Delivery',1820,1 union all
27select 'COS - Inventory Adj - Stock Count',1910,1 union all
28select 'COS - Inv. Adj - Stock Write up / Write down',1920,1 union all
29select 'COS - Provision for Obsolete Stock (IS)',1930,1 union all
30select 'COS - Inventory Adj - System A/c',1996,1 union all
31select 'COS - Purch & Dir. Cost Appl A/c - System A/c',1997,1 union all
32select 'GROSS MARGIN',1999,0 union all
33select 'OTHER INCOME',2000,0 union all
34select 'Admin Fees Received',2100,1 union all
35select 'Bad Debt Recovered',2110,1 union all
36select 'Discount Received',2120,1 union all
37select 'Dividends Received',2130,1 union all
38select 'Fixed Assets - NBV on Disposal',2140,1 union all
39select 'Fixed Assets - Proceeds on Disposal',2145,1 union all
40select 'Rebates Received',2150,1 union all
41select 'Rental Income',2160,1 union all
42select 'Sundry Income',2170,1 union all
43select 'Warranty Income',2180,1 union all
44select 'INTEREST RECEIVED',2200,0 union all
45select 'Interest Received - Banks',2210,1
46)
47
48select
49 Account
50, AccountNumber
51, Indentation
52from source;
53Using the following script:
1with source (Account,AccountNumber,Indentation) as
2(
3select 'INCOME STATEMENT',1000,0 union all
4select 'REVENUE',1100,0 union all
5select 'Revenue - Aircon',1110,1 union all
6select 'Revenue - Consumer Goods',1120,1 union all
7select 'Revenue - Spares',1130,1 union all
8select 'Revenue - Accessories',1140,1 union all
9select 'Revenue - Sub Stock',1150,1 union all
10select 'Revenue - Services',1160,1 union all
11select 'Revenue - Other',1170,1 union all
12select 'Revenue - Intercompany',1180,1 union all
13select 'Revenue - Delivery Charges',1400,1 union all
14select 'COST OF SALES',1500,0 union all
15select 'COGS - Aircon',1510,1 union all
16select 'COGS - Consumer Goods',1520,1 union all
17select 'COGS - Spares',1530,1 union all
18select 'COGS - Accessories',1540,1 union all
19select 'COGS - Sub Stock',1550,1 union all
20select 'COGS - Services',1560,1 union all
21select 'COGS - Other',1570,1 union all
22select 'COGS - Intercompany',1580,1 union all
23select 'COS - Sub Stock Stock Adjustments',1610,1 union all
24select 'COS - Sub Stock Repairs',1620,1 union all
25select 'COS - Consumables & Packing Materials',1810,1 union all
26select 'COS - Freight & Delivery',1820,1 union all
27select 'COS - Inventory Adj - Stock Count',1910,1 union all
28select 'COS - Inv. Adj - Stock Write up / Write down',1920,1 union all
29select 'COS - Provision for Obsolete Stock (IS)',1930,1 union all
30select 'COS - Inventory Adj - System A/c',1996,1 union all
31select 'COS - Purch & Dir. Cost Appl A/c - System A/c',1997,1 union all
32select 'GROSS MARGIN',1999,0 union all
33select 'OTHER INCOME',2000,0 union all
34select 'Admin Fees Received',2100,1 union all
35select 'Bad Debt Recovered',2110,1 union all
36select 'Discount Received',2120,1 union all
37select 'Dividends Received',2130,1 union all
38select 'Fixed Assets - NBV on Disposal',2140,1 union all
39select 'Fixed Assets - Proceeds on Disposal',2145,1 union all
40select 'Rebates Received',2150,1 union all
41select 'Rental Income',2160,1 union all
42select 'Sundry Income',2170,1 union all
43select 'Warranty Income',2180,1 union all
44select 'INTEREST RECEIVED',2200,0 union all
45select 'Interest Received - Banks',2210,1
46)
47
48select
49 Account
50, AccountNumber
51, Indentation
52from source;
53with s as (
54select
55 iif(Account like 'Total%',null,iif(Indentation=0,Account,null)) Header
56, iif(Account like 'Total%',null,iif(Indentation=1,Account,null)) SubHeader1
57, *
58from Source
59)
60
61select
62 Header
63--, case lag(Header) over (order by [Account Number]) when Header then isnull(Header,lag(Header) over (order by [Account Number])) else Header end
64, SubHeader1
65, [Account Number]
66, Indentation
67from s
68I'm able to split the columns like this:
I need to be able to report the Header Column to look like this:
I tried doing it using LAG(), but it doesn't work, how would I script this?
ANSWER
Answered 2021-Nov-12 at 12:59This is one option. I created a Group for each header and then used it to grab the first in that group that had an Indention = 0. Tack this on to your source CTE:
1with source (Account,AccountNumber,Indentation) as
2(
3select 'INCOME STATEMENT',1000,0 union all
4select 'REVENUE',1100,0 union all
5select 'Revenue - Aircon',1110,1 union all
6select 'Revenue - Consumer Goods',1120,1 union all
7select 'Revenue - Spares',1130,1 union all
8select 'Revenue - Accessories',1140,1 union all
9select 'Revenue - Sub Stock',1150,1 union all
10select 'Revenue - Services',1160,1 union all
11select 'Revenue - Other',1170,1 union all
12select 'Revenue - Intercompany',1180,1 union all
13select 'Revenue - Delivery Charges',1400,1 union all
14select 'COST OF SALES',1500,0 union all
15select 'COGS - Aircon',1510,1 union all
16select 'COGS - Consumer Goods',1520,1 union all
17select 'COGS - Spares',1530,1 union all
18select 'COGS - Accessories',1540,1 union all
19select 'COGS - Sub Stock',1550,1 union all
20select 'COGS - Services',1560,1 union all
21select 'COGS - Other',1570,1 union all
22select 'COGS - Intercompany',1580,1 union all
23select 'COS - Sub Stock Stock Adjustments',1610,1 union all
24select 'COS - Sub Stock Repairs',1620,1 union all
25select 'COS - Consumables & Packing Materials',1810,1 union all
26select 'COS - Freight & Delivery',1820,1 union all
27select 'COS - Inventory Adj - Stock Count',1910,1 union all
28select 'COS - Inv. Adj - Stock Write up / Write down',1920,1 union all
29select 'COS - Provision for Obsolete Stock (IS)',1930,1 union all
30select 'COS - Inventory Adj - System A/c',1996,1 union all
31select 'COS - Purch & Dir. Cost Appl A/c - System A/c',1997,1 union all
32select 'GROSS MARGIN',1999,0 union all
33select 'OTHER INCOME',2000,0 union all
34select 'Admin Fees Received',2100,1 union all
35select 'Bad Debt Recovered',2110,1 union all
36select 'Discount Received',2120,1 union all
37select 'Dividends Received',2130,1 union all
38select 'Fixed Assets - NBV on Disposal',2140,1 union all
39select 'Fixed Assets - Proceeds on Disposal',2145,1 union all
40select 'Rebates Received',2150,1 union all
41select 'Rental Income',2160,1 union all
42select 'Sundry Income',2170,1 union all
43select 'Warranty Income',2180,1 union all
44select 'INTEREST RECEIVED',2200,0 union all
45select 'Interest Received - Banks',2210,1
46)
47
48select
49 Account
50, AccountNumber
51, Indentation
52from source;
53with s as (
54select
55 iif(Account like 'Total%',null,iif(Indentation=0,Account,null)) Header
56, iif(Account like 'Total%',null,iif(Indentation=1,Account,null)) SubHeader1
57, *
58from Source
59)
60
61select
62 Header
63--, case lag(Header) over (order by [Account Number]) when Header then isnull(Header,lag(Header) over (order by [Account Number])) else Header end
64, SubHeader1
65, [Account Number]
66, Indentation
67from s
68,CTE2 AS
69(
70select
71 iif(Account like 'Total%',null,iif(Indentation=0,Account,null)) Header
72, iif(Account like 'Total%',null,iif(Indentation=1,Account,null)) SubHeader1
73, SUM(CASE WHEN Indentation = 0 THEN 1 ELSE 0 END) OVER (ORDER BY AccountNUmber) H1
74, *
75from Source
76)
77
78SELECT T2.Header, t1.SubHeader1, t1.AccountNumber, t1.Indentation
79FROM CTE2 t1
80CROSS APPLY(SELECT MAX(t3.HEADER) HEADER FROM CTE2 T3 where t3.H1 = T1.H1 and T3.Indentation = 0 ) T2
81ORDER BY t1.AccountNumber
82QUESTION
how to break up data in column value to multiple rows in pandas dataframe
Asked 2021-Oct-12 at 01:09I have an issue where I have multiple rows in a csv file that have to be converted to a pandas data frame but there are some rows where the columns 'name' and 'business' have multiple names and businesses that should be in separate rows and need to be split up while keeping the data from the other columns the same for each row that is split.
Here is the example data:
input:
| software | name | business |
|---|---|---|
| abc | Andrew Johnson, Steve Martin | Outsourcing/Offshoring, 201-500 employees,Health, Wellness and Fitness, 5001-10,000 employees |
| xyz | Jack Jones, Rick Paul, Johnny Jones | Banking, 1001-5000 employees,Construction, 51-200 employees,Consumer Goods, 10,001+ employees |
| def | Tom D., Connie J., Ricky B. | Unspecified, Unspecified, Self-employed |
output I need:
| software | name | business |
|---|---|---|
| abc | Andrew Johnson | Outsourcing/Offshoring, 201-500 employees |
| abc | Steve Martin | Health, Wellness and Fitness, 5001-10,000 employees |
| xyz | Jack Jones | Banking, 1001-5000 employees |
| xyz | Rick Paul | Construction, 51-200 employees |
| xyz | Johnny Jones | Consumer Goods, 10,001+ employees |
| def | Tom D | Unspecified |
| def | Connie J | Unspecified |
| def | Ricky B | Self-employed |
There are additional columns similar to 'name' and 'business' that contain multiple pieces of information that need to be split up just like 'name' and 'business'. Cells that contain multiple pieces of information are in sequence (ordered).
Here's the code I have so far and creates new rows but it only splits up the contents in name column, but that leaves the business column and a few other columns left over that need to be split up along with the contents from the name column.
1name2 = df.name.str.split(',', expand=True).stack()
2df = df.join(pd.Series(index=name2.index.droplevel(1), data=name2.values, name = 'name2'))
3
4dict = df.to_dict('record')
5for row in dict:
6 new_segment = {}
7 new_segment['name'] = str(row['name2'])
8 #df['name'] = str(row['name2'])
9
10 for col,content in new_segment.items():
11 row[col] = content
12
13df = pd.DataFrame.from_dict(dict)
14
15df = df.drop('name2', 1)
16Here's an alternative solution I was trying as well but it gives me an error too:
1name2 = df.name.str.split(',', expand=True).stack()
2df = df.join(pd.Series(index=name2.index.droplevel(1), data=name2.values, name = 'name2'))
3
4dict = df.to_dict('record')
5for row in dict:
6 new_segment = {}
7 new_segment['name'] = str(row['name2'])
8 #df['name'] = str(row['name2'])
9
10 for col,content in new_segment.items():
11 row[col] = content
12
13df = pd.DataFrame.from_dict(dict)
14
15df = df.drop('name2', 1)
16review_path = r'data/base_data'
17review_files = glob.glob(review_path + "/test_data.csv")
18
19review_df_list = []
20for review_file in review_files:
21 df = pd.read_csv(io.StringIO(review_file), sep = '\t')
22 print(df.head())
23 df["business"] = (df["business"].str.extractall(r"(?:[\s,]*)(.*?(?:Unspecified|employees|Self-employed))").groupby(level=0).agg(list))
24 df["name"] = df["name"].str.split(r"\s*,\s*")
25 print(df.explode(["name", "business"]))
26 outPutPath = Path('data/base_data/test_data.csv')
27 df.to_csv(outPutPath, index=False)
28Error Message for alternative solution:
Read:data/base_data/review_base.csv
Success!
Empty DataFrame
Columns: [data/base_data/test_data.csv]
Index: []
ANSWER
Answered 2021-Oct-10 at 23:11Try:
1name2 = df.name.str.split(',', expand=True).stack()
2df = df.join(pd.Series(index=name2.index.droplevel(1), data=name2.values, name = 'name2'))
3
4dict = df.to_dict('record')
5for row in dict:
6 new_segment = {}
7 new_segment['name'] = str(row['name2'])
8 #df['name'] = str(row['name2'])
9
10 for col,content in new_segment.items():
11 row[col] = content
12
13df = pd.DataFrame.from_dict(dict)
14
15df = df.drop('name2', 1)
16review_path = r'data/base_data'
17review_files = glob.glob(review_path + "/test_data.csv")
18
19review_df_list = []
20for review_file in review_files:
21 df = pd.read_csv(io.StringIO(review_file), sep = '\t')
22 print(df.head())
23 df["business"] = (df["business"].str.extractall(r"(?:[\s,]*)(.*?(?:Unspecified|employees|Self-employed))").groupby(level=0).agg(list))
24 df["name"] = df["name"].str.split(r"\s*,\s*")
25 print(df.explode(["name", "business"]))
26 outPutPath = Path('data/base_data/test_data.csv')
27 df.to_csv(outPutPath, index=False)
28df["business"] = (
29 df["business"]
30 .str.extractall(r"(?:[\s,]*)(.*?(?:Unspecified|employees|Self-employed))")
31 .groupby(level=0)
32 .agg(list)
33)
34df["name"] = df["name"].str.split(r"\s*,\s*")
35
36print(df.explode(["name", "business"]))
37Prints:
1name2 = df.name.str.split(',', expand=True).stack()
2df = df.join(pd.Series(index=name2.index.droplevel(1), data=name2.values, name = 'name2'))
3
4dict = df.to_dict('record')
5for row in dict:
6 new_segment = {}
7 new_segment['name'] = str(row['name2'])
8 #df['name'] = str(row['name2'])
9
10 for col,content in new_segment.items():
11 row[col] = content
12
13df = pd.DataFrame.from_dict(dict)
14
15df = df.drop('name2', 1)
16review_path = r'data/base_data'
17review_files = glob.glob(review_path + "/test_data.csv")
18
19review_df_list = []
20for review_file in review_files:
21 df = pd.read_csv(io.StringIO(review_file), sep = '\t')
22 print(df.head())
23 df["business"] = (df["business"].str.extractall(r"(?:[\s,]*)(.*?(?:Unspecified|employees|Self-employed))").groupby(level=0).agg(list))
24 df["name"] = df["name"].str.split(r"\s*,\s*")
25 print(df.explode(["name", "business"]))
26 outPutPath = Path('data/base_data/test_data.csv')
27 df.to_csv(outPutPath, index=False)
28df["business"] = (
29 df["business"]
30 .str.extractall(r"(?:[\s,]*)(.*?(?:Unspecified|employees|Self-employed))")
31 .groupby(level=0)
32 .agg(list)
33)
34df["name"] = df["name"].str.split(r"\s*,\s*")
35
36print(df.explode(["name", "business"]))
37 software name business
380 abc Andrew Johnson Outsourcing/Offshoring, 201-500 employees
390 abc Steve Martin Health, Wellness and Fitness, 5001-10,000 employees
401 xyz Jack Jones Banking, 1001-5000 employees
411 xyz Rick Paul Construction, 51-200 employees
421 xyz Johnny Jones Consumer Goods, 10,001+ employees
432 def Tom D. Unspecified
442 def Connie J. Unspecified
452 def Ricky B. Self-employed
46QUESTION
Python Sorting through nested dictionary
Asked 2020-May-29 at 15:30From multiple tables I'm getting values of the form: (Sector, Stock, InvestedValue) I'm using python dictionary object to insert these values and during insert if the combination (sector, stock) exists add the InvestedValue to the existing entry else add new entry to dictionary. After all data insert, let's say I end up with nested dictionary like:
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6How to print through this nested dictionary in sorted fashion:
Get combination of Sector,Company,InvestedValue sorted on the basis of InvestedValue
Get combination of Sector, sum(InvestedValue) ie. sum of InvestedValue of each company in that sector again sorted on the sum
My current approach to solve these problems is to flatten the nested dictionary to list of tupples and run sorted on it. For example:
To solve 1:
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11To solve 2:
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11sector_list = []
12for sector in stock_dict:
13 sector_list.append((sector,sum(stock_dict[sector].values())))
14sorted_sector_list = sorted(sector_list, key=lambda sector: sector[1], reverse=True)
15IS there a better approach i.e. sort directly on the nested dictionary without having to flatten it into the list ?
ANSWER
Answered 2020-May-29 at 15:30If you uses pandas, it can convert you dict to a dataframe. You can then unstack the dataframe which will make it long form instead of wide. Once it's in the long format, you will have null values for any sector which a company does not participate in, you can drop those with dropna(), reset the index and sort values by your desired column. Once this is done rename your columns to the desired names, and select them in the order you want. You can take that same df and groupby sector and sum the InvestedValue.
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11sector_list = []
12for sector in stock_dict:
13 sector_list.append((sector,sum(stock_dict[sector].values())))
14sorted_sector_list = sorted(sector_list, key=lambda sector: sector[1], reverse=True)
15import pandas as pd
16stock_dict = {
17 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
18 "Automobile": {"Tata Motors": 135.67},
19 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
20}
21
22df = pd.DataFrame.from_dict(stock_dict, orient='index')
23df = df.unstack().dropna().reset_index(name='InvestedValue').sort_values(by='InvestedValue', ascending=False)
24
25df.columns = ['Company','Sector','InvestedValue']
26df[['Sector','Company','InvestedValue']]
27Output
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11sector_list = []
12for sector in stock_dict:
13 sector_list.append((sector,sum(stock_dict[sector].values())))
14sorted_sector_list = sorted(sector_list, key=lambda sector: sector[1], reverse=True)
15import pandas as pd
16stock_dict = {
17 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
18 "Automobile": {"Tata Motors": 135.67},
19 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
20}
21
22df = pd.DataFrame.from_dict(stock_dict, orient='index')
23df = df.unstack().dropna().reset_index(name='InvestedValue').sort_values(by='InvestedValue', ascending=False)
24
25df.columns = ['Company','Sector','InvestedValue']
26df[['Sector','Company','InvestedValue']]
27 Sector Company InvestedValue
280 Financial HDFC Bank 230.25
294 Consumer Goods Avenue Supermarket 190.45
303 Automobile Tata Motors 135.67
315 Consumer Goods Godrej Industries 120.32
322 Financial ICICI Bank 110.45
331 Financial Axis Bank 70.15
342nd Part
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11sector_list = []
12for sector in stock_dict:
13 sector_list.append((sector,sum(stock_dict[sector].values())))
14sorted_sector_list = sorted(sector_list, key=lambda sector: sector[1], reverse=True)
15import pandas as pd
16stock_dict = {
17 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
18 "Automobile": {"Tata Motors": 135.67},
19 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
20}
21
22df = pd.DataFrame.from_dict(stock_dict, orient='index')
23df = df.unstack().dropna().reset_index(name='InvestedValue').sort_values(by='InvestedValue', ascending=False)
24
25df.columns = ['Company','Sector','InvestedValue']
26df[['Sector','Company','InvestedValue']]
27 Sector Company InvestedValue
280 Financial HDFC Bank 230.25
294 Consumer Goods Avenue Supermarket 190.45
303 Automobile Tata Motors 135.67
315 Consumer Goods Godrej Industries 120.32
322 Financial ICICI Bank 110.45
331 Financial Axis Bank 70.15
34df.groupby('Sector')['InvestedValue'].sum().reset_index().sort_values(by='InvestedValue', ascending=False)
35Output
1stock_dict = {
2 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
3 "Automobile": {"Tata Motors": 135.67},
4 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
5}
6stock_list = []
7for sector in stock_dict:
8 for stock in stock_dict[sector]:
9 stock_list.append((sector, stock, stock_dict[sector][stock]))
10sorted_list = sorted(stock_list, key=lambda stock: stock[2], reverse=True)
11sector_list = []
12for sector in stock_dict:
13 sector_list.append((sector,sum(stock_dict[sector].values())))
14sorted_sector_list = sorted(sector_list, key=lambda sector: sector[1], reverse=True)
15import pandas as pd
16stock_dict = {
17 "Financial": {"HDFC Bank": 230.25, "Axis Bank": 70.15, "ICICI Bank": 110.45},
18 "Automobile": {"Tata Motors": 135.67},
19 "Consumer Goods": {"Avenue Supermarket": 190.45, "Godrej Industries": 120.32}
20}
21
22df = pd.DataFrame.from_dict(stock_dict, orient='index')
23df = df.unstack().dropna().reset_index(name='InvestedValue').sort_values(by='InvestedValue', ascending=False)
24
25df.columns = ['Company','Sector','InvestedValue']
26df[['Sector','Company','InvestedValue']]
27 Sector Company InvestedValue
280 Financial HDFC Bank 230.25
294 Consumer Goods Avenue Supermarket 190.45
303 Automobile Tata Motors 135.67
315 Consumer Goods Godrej Industries 120.32
322 Financial ICICI Bank 110.45
331 Financial Axis Bank 70.15
34df.groupby('Sector')['InvestedValue'].sum().reset_index().sort_values(by='InvestedValue', ascending=False)
35 Sector InvestedValue
362 Financial 410.85
371 Consumer Goods 310.77
380 Automobile 135.67
39QUESTION
R: How can I lower the amount of levels in a dataframe?
Asked 2020-Apr-30 at 18:31I have a column in a dataframe with over 40 levels, I want to make it 4 levels. The important variables are "ecommerce", "technology", and "consumer goods", everything else I want to fall under "other". How can I make it into 4 levels?
ANSWER
Answered 2020-Apr-30 at 02:33We can use %in% to check :
1df$column_name <- as.character(df$column_name)
2df$column_name[!df$column_name %in% c('ecommerce', 'technology', 'consumer goods')] <- 'Other'
3If you want to keep the column as factors :
1df$column_name <- as.character(df$column_name)
2df$column_name[!df$column_name %in% c('ecommerce', 'technology', 'consumer goods')] <- 'Other'
3levels(df$column_name) <- c(levels(df$column_name), 'Other')
4df$column_name[!df$column_name %in% c('ecommerce', 'technology', 'consumer goods')] <- 'Other'
5QUESTION
R: How can I make a column with lots of categorical levels binary?
Asked 2020-Apr-29 at 22:29I would like to have "ecommerce", "consumer goods" and "technology" equal 1, and all other industries equal 0. There are a ton of levels within the Industry.Vertical column, how can I make it binary?
A little piece of the dataset:
1*Industry.Vertical* *City..Location*
2technology Andheri
3healthcare Mumbai
4luxury label Mumbai
5technology Chembur
6ecommerce Bengaluru
7food & beverages New Delhi
8ecommerce Gurgaon
9finance Bengaluru
10finance New Delhi
11waste management service Hyderabad
12technology Bengaluru
13agriculture Nairobi
14energy New Delhi
15ANSWER
Answered 2020-Apr-29 at 04:40We can use %in% to check if Industry column has any of c("ecommerce", "consumer goods", "technology") values and turn them to integers.
1*Industry.Vertical* *City..Location*
2technology Andheri
3healthcare Mumbai
4luxury label Mumbai
5technology Chembur
6ecommerce Bengaluru
7food & beverages New Delhi
8ecommerce Gurgaon
9finance Bengaluru
10finance New Delhi
11waste management service Hyderabad
12technology Bengaluru
13agriculture Nairobi
14energy New Delhi
15df$new_col <- as.integer(df$Industry %in% c("ecommerce", "consumer goods", "technology"))
16which is faster than using ifelse :
1*Industry.Vertical* *City..Location*
2technology Andheri
3healthcare Mumbai
4luxury label Mumbai
5technology Chembur
6ecommerce Bengaluru
7food & beverages New Delhi
8ecommerce Gurgaon
9finance Bengaluru
10finance New Delhi
11waste management service Hyderabad
12technology Bengaluru
13agriculture Nairobi
14energy New Delhi
15df$new_col <- as.integer(df$Industry %in% c("ecommerce", "consumer goods", "technology"))
16df$new_col <- ifelse(df$Industry %in% c("ecommerce", "consumer goods", "technology"), 1, 0)
17Community Discussions contain sources that include Stack Exchange Network
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