title: "Food Points" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
data = {
'dinner' : ['chicken wrap', 'stake', 'rudy burger', 'sushi', 'chicken teriyaki', 'caesar salad']
}
df = pd.DataFrame(data)
df
Category: data-wrangling
Read Moretitle: "Groupby Date" author: "Rj" date: 2019-04-20 description: "List Test" type: technical_note draft: false
from datetime import datetime
import pandas as pd
data = {
'date' : [
'2019-05-01 19:47:05.069722',
'2019-05-02 17:47:05.069722',
'2019-05-02 19:47:05.069722',
'2019-05-03 18:47:05.069722',
'2019-05-03 19:47:05.069722',
],
'spent …Category: data-wrangling
Read Moretitle: "If Else Pandas" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false source: http://pandas.pydata.org/pandas-docs/version/0.24/user_guide/cookbook.html#idioms
import numpy as np
import pandas as pd
df = pd.DataFrame({
'maths' : [80, 89, 90, 20],
'science' : [40, 50, 90, 100],
'language' : [20, 30 …Category: data-wrangling
Read Moretitle: "Template" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false source: http://pandas.pydata.org/pandas-docs/version/0.24/user_guide/cookbook.html#idioms
import numpy as np
import pandas as pd
df = pd.read_csv('abc.csv')
df
Category: data-wrangling
Read Moretitle: "iLoc Simple" author: "Rj" date: 2019-04-20 description: "-" type: technical_note draft: false
import pandas as pd
df = pd.read_csv('data1.csv', sep=',', header=None)
df
| 0 | 1 | 2 | |
|---|---|---|---|
| 0 | capacity | score | length … |
Category: data-wrangling
Read Moretitle: "iLoc Position Slice" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
df = pd.read_csv('data1.csv')
df
| capacity | score | length | |
|---|---|---|---|
| 0 | 1 … |
Category: data-wrangling
Read Moretitle: "Index isin" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false source: http://pandas.pydata.org/pandas-docs/version/0.24/user_guide/cookbook.html#idioms
import numpy as np
import pandas as pd
df = pd.DataFrame({
'maths' : [80, 89, 90, 20, 60],
'science' : [40, 50, 90, 100, 50],
'language' : [20 …Category: data-wrangling
Read Moretitle: "Infer Objects" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
df
Category: data-wrangling
Read Moretitle: "Initalize With DataType" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
df
Category: data-wrangling
Read Moretitle: "Int and NaN" author: "Rj" date: 2019-04-24 description: "-" type: technical_note draft: false
import numpy as np
import pandas as pd
df = pd.DataFrame({
'one': [4, 5],
'two': [10, 20]
})
df
| one … |
|---|
Category: data-wrangling
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