I'm using Django 2.2.
I want to generate the analytics of the number of records by each day between the stand and end date.
The query used is
start_date = '2021-9-1'
end_date = '2021-9-30'
query = Tracking.objects.filter(
scan_time__date__gte=start_date,
scan_time__date__lte=end_date
)
query.annotate(
scanned_date=TruncDate('scan_time')
).order_by(
'scanned_date'
).values('scanned_date').annotate(
**{'total': Count('created')}
)
Which produces output as
[{'scanned_date': datetime.date(2021, 9, 24), 'total': 5}, {'scanned_date': datetime.date(2021, 9, 26), 'total': 3}]
I want to fill the missing dates with 0, so that the output should be
2021-9-1: 0
2021-9-2: 0
...
2021-9-24: 5
2021-9-25: 0
2021-9-26: 3
...
2021-9-30: 0
How I can achieve this using either ORM or python (ie., pandas, etc.)?
Use DataFrame.reindex by date range created by date_range with DatetimeIndex by DataFrame.set_index:
data = [{'scanned_date': datetime.date(2021, 9, 24), 'total': 5},
{'scanned_date': datetime.date(2021, 9, 26), 'total': 3}]
start_date = '2021-9-1'
end_date = '2021-9-30'
r = pd.date_range(start_date, end_date, name='scanned_date')
#if necessary convert to dates from datetimes
#r = pd.date_range(start_date, end_date, name='scanned_date').date
df = pd.DataFrame(data).set_index('scanned_date').reindex(r, fill_value=0).reset_index()
print (df)
scanned_date total
0 2021-09-01 0
1 2021-09-02 0
2 2021-09-03 0
3 2021-09-04 0
4 2021-09-05 0
5 2021-09-06 0
6 2021-09-07 0
7 2021-09-08 0
8 2021-09-09 0
9 2021-09-10 0
10 2021-09-11 0
11 2021-09-12 0
12 2021-09-13 0
13 2021-09-14 0
14 2021-09-15 0
15 2021-09-16 0
16 2021-09-17 0
17 2021-09-18 0
18 2021-09-19 0
19 2021-09-20 0
20 2021-09-21 0
21 2021-09-22 0
22 2021-09-23 0
23 2021-09-24 5
24 2021-09-25 0
25 2021-09-26 3
26 2021-09-27 0
27 2021-09-28 0
28 2021-09-29 0
29 2021-09-30 0
Or use left join by another DataFrame create from range with replace misisng values to 0:
r = pd.date_range(start_date, end_date, name='scanned_date').date
df = pd.DataFrame({'scanned_date':r}).merge(pd.DataFrame(data), how='left', on='scanned_date').fillna(0)
Related
Using Power Query "M" language, how would you transform a categorical column containing discrete values into multiple "dummy" columns? I come from the Python world and there are several ways to do this but one way would be below:
>>> import pandas as pd
>>> dataset = pd.DataFrame(list('ABCDACDEAABADDA'),
columns=['my_col'])
>>> dataset
my_col
0 A
1 B
2 C
3 D
4 A
5 C
6 D
7 E
8 A
9 A
10 B
11 A
12 D
13 D
14 A
>>> pd.get_dummies(dataset)
my_col_A my_col_B my_col_C my_col_D my_col_E
0 1 0 0 0 0
1 0 1 0 0 0
2 0 0 1 0 0
3 0 0 0 1 0
4 1 0 0 0 0
5 0 0 1 0 0
6 0 0 0 1 0
7 0 0 0 0 1
8 1 0 0 0 0
9 1 0 0 0 0
10 0 1 0 0 0
11 1 0 0 0 0
12 0 0 0 1 0
13 0 0 0 1 0
14 1 0 0 0 0
Interesting question. Here's an easy, scalable method I've found:
Create a custom column of all ones (Add Column > Custom Column > Formula = 1).
Add an index column (Add Column > Index Column).
Pivot on the custom column (select my_col > Transform > Pivot Column).
Replace null values with 0 (select all columns > Transform > Replace Values).
Here's what the M code looks like for this process:
#"Added Custom" = Table.AddColumn(#"Previous Step", "Custom", each 1),
#"Added Index" = Table.AddIndexColumn(#"Added Custom", "Index", 0, 1),
#"Pivoted Column" = Table.Pivot(#"Added Index", List.Distinct(#"Added Index"[my_col]), "my_col", "Custom"),
#"Replaced Value" = Table.ReplaceValue(#"Pivoted Column",null,0,Replacer.ReplaceValue,Table.ColumnNames(#"Pivoted Column"))
Once you've completed the above, you can remove the index column if desired.
>>> df.head()
№ Summer Gold Silver Bronze Total № Winter \
Afghanistan (AFG) 13 0 0 2 2 0
Algeria (ALG) 12 5 2 8 15 3
Argentina (ARG) 23 18 24 28 70 18
Armenia (ARM) 5 1 2 9 12 6
Australasia (ANZ) [ANZ] 2 3 4 5 12 0
Gold.1 Silver.1 Bronze.1 Total.1 № Games Gold.2 \
Afghanistan (AFG) 0 0 0 0 13 0
Algeria (ALG) 0 0 0 0 15 5
Argentina (ARG) 0 0 0 0 41 18
Armenia (ARM) 0 0 0 0 11 1
Australasia (ANZ) [ANZ] 0 0 0 0 2 3
Silver.2 Bronze.2 Combined total
Afghanistan (AFG) 0 2 2
Algeria (ALG) 2 8 15
Argentina (ARG) 24 28 70
Armenia (ARM) 2 9 12
Australasia (ANZ) [ANZ] 4 5 12
Not sure why do I see this error:
>>> df['Gold'] > 0 | df['Gold.1'] > 0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/ankuragarwal/data_insight/env/lib/python2.7/site-packages/pandas/core/generic.py", line 917, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
Whats ambiguous here ?
But this works:
>>> (df['Gold'] > 0) | (df['Gold.1'] > 0)
Assuming we have the following DF:
In [35]: df
Out[35]:
a b c
0 9 0 1
1 7 7 4
2 1 8 9
3 6 7 5
4 1 4 6
The following command:
df.a > 5 | df.b > 5
because | has higher precedence (compared to >) as it's specified in the Operator precedence table) it will be translated to:
df.a > (5 | df.b) > 5
which will be translated to:
df.a > (5 | df.b) and (5 | df.b) > 5
step by step:
In [36]: x = (5 | df.b)
In [37]: x
Out[37]:
0 5
1 7
2 13
3 7
4 5
Name: c, dtype: int32
In [38]: df.a > x
Out[38]:
0 True
1 False
2 False
3 False
4 False
dtype: bool
In [39]: x > 5
Out[39]:
0 False
1 True
2 True
3 True
4 False
Name: b, dtype: bool
but the last operation won't work:
In [40]: (df.a > x) and (x > 5)
---------------------------------------------------------------------------
...
skipped
...
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
The error message above might lead inexperienced users to do something like this:
In [12]: (df.a > 5).all() | (df.b > 5).all()
Out[12]: False
In [13]: df[(df.a > 5).all() | (df.b > 5).all()]
...
skipped
...
KeyError: False
But in this case you just need to set your precedence explicitly in order to get expected result:
In [10]: (df.a > 5) | (df.b > 5)
Out[10]:
0 True
1 True
2 True
3 True
4 False
dtype: bool
In [11]: df[(df.a > 5) | (df.b > 5)]
Out[11]:
a b c
0 9 0 1
1 7 7 4
2 1 8 9
3 6 7 5
This is the real reason for the error:
http://pandas.pydata.org/pandas-docs/stable/gotchas.html
pandas follows the numpy convention of raising an error when you try to convert something to a bool. This happens in a if or when using the boolean operations, and, or, or not. It is not clear what the result of
>>> if pd.Series([False, True, False]):
...
should be. Should it be True because it’s not zero-length? False because there are False values? It is unclear, so instead, pandas raises a ValueError:
>>> if pd.Series([False, True, False]):
print("I was true")
Traceback
...
ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
If you see that, you need to explicitly choose what you want to do with it (e.g., use any(), all() or empty). or, you might want to compare if the pandas object is None
I have a data frame like follow:
pop state value1 value2
0 1.8 Ohio 2000001 2100345
1 1.9 Ohio 2001001 1000524
2 3.9 Nevada 2002100 1000242
3 2.9 Nevada 2001003 1234567
4 2.0 Nevada 2002004 1420000
And I have a ordered dictionary like following:
OrderedDict([(1, OrderedDict([('value1_1', [1, 2]),('value1_2', [3, 4]),('value1_3',[5,7])])),(1, OrderedDict([('value2_1', [1, 1]),('value2_2', [2, 5]),('value2_3',[6,7])]))])
I want to changed the data frame as the OrderedDict needed.
pop state value1_1 value1_2 value1_3 value2_1 value2_2 value2_3
0 1.8 Ohio 20 0 1 2 1003 45
1 1.9 Ohio 20 1 1 1 5 24
2 3.9 Nevada 20 2 100 1 2 42
3 2.9 Nevada 20 1 3 1 2345 67
4 2.0 Nevada 20 2 4 1 4200 0
I think it is really a complex logic in python pandas. How can I solve it? Thanks.
First, your OrderedDict overwrites the same key, you need to use different keys.
d= OrderedDict([(1, OrderedDict([('value1_1', [1, 2]),('value1_2', [3, 4]),('value1_3',[5,7])])),(2, OrderedDict([('value2_1', [1, 1]),('value2_2', [2, 5]),('value2_3',[6,7])]))])
Now, for your actual problem, you can iterate through d to get the items, and use the apply function on the DataFrame to get what you need.
for k,v in d.items():
for k1,v1 in v.items():
if k == 1:
df[k1] = df.value1.apply(lambda x : int(str(x)[v1[0]-1:v1[1]]))
else:
df[k1] = df.value2.apply(lambda x : int(str(x)[v1[0]-1:v1[1]]))
Now, df is
pop state value1 value2 value1_1 value1_2 value1_3 value2_1 \
0 1.8 Ohio 2000001 2100345 20 0 1 2
1 1.9 Ohio 2001001 1000524 20 1 1 1
2 3.9 Nevada 2002100 1000242 20 2 100 1
3 2.9 Nevada 2001003 1234567 20 1 3 1
4 2.0 Nevada 2002004 1420000 20 2 4 1
value2_2 value2_3
0 1003 45
1 5 24
2 2 42
3 2345 67
4 4200 0
I think this would point you in the right direction.
Converting the value1 and value2 columns to string type:
df['value1'], df['value2'] = df['value1'].astype(str), df['value2'].astype(str)
dct_1,dct_2 = OrderedDict([('value1_1', [1, 2]),('value1_2', [3, 4]),('value1_3',[5,7])]),
OrderedDict([('value2_1', [1, 1]),('value2_2', [2, 5]),('value2_3',[6,7])])
Converting Ordered Dictionary to a list of tuples:
dct_1_list, dct_2_list = list(dct_1.items()), list(dct_2.items())
Flattening a list of lists to a single list:
L1, L2 = sum(list(x[1] for x in dct_1_list), []), sum(list(x[1] for x in dct_2_list), [])
Subtracting the even slices of the list by 1 as the string indices start from 0 and not 1:
L1[::2], L2[::2] = np.array(L1[0::2]) - np.array([1]), np.array(L2[0::2]) - np.array([1])
Taking the appropriate slice positions and mapping those values to the newly created columns of the dataframe:
df['value1_1'],df['value1_2'],df['value1_3']= map(df['value1'].str.slice,L1[::2],L1[1::2])
df['value2_1'],df['value2_2'],df['value2_3']= map(df['value2'].str.slice,L2[::2],L2[1::2])
Dropping off unwanted columns:
df.drop(['value1', 'value2'], axis=1, inplace=True)
Final result:
print(df)
pop state value1_1 value1_2 value1_3 value2_1 value2_2 value2_3
0 1.8 Ohio 20 00 001 2 1003 45
1 1.9 Ohio 20 01 001 1 0005 24
2 3.9 Nevada 20 02 100 1 0002 42
3 2.9 Nevada 20 01 003 1 2345 67
4 2.0 Nevada 20 02 004 1 4200 00
assume I have a dataframe looks like below.
df = pd.DataFrame({
'name' : ['1st', '2nd', '3rd'],
'john_01' : [1, 2, 3],
'mary_02' : [4,5,6],
'peter_03' : [7, 8, 9],
'roger_04' : [10,11, 12],
'ken_05' : [13, 14, 15],
})
df2 = df.set_index('name')
john_01 ken_05 mary_02 peter_03 roger_04
name
1st 1 13 4 7 10
2nd 2 14 5 8 11
3rd 3 15 6 9 12
Modify_List_col = ['mary_02','peter_03']
Modify_List_row = ['2nd'] # use tolist() to get this list from additional files
I only want to modify those cells in List_col and List_row. So I will get something like below, those cells are replaced by 'X'.
john_01 ken_05 mary_02 peter_03 roger_04
name
1st 1 13 4 7 10
2nd 2 14 X X 11
3rd 3 15 6 9 12
Does anyone know how to get the results in one line using pandas please?
You can use the loc method:
In[25]: df = pd.DataFrame(pd.np.arange(25).reshape(5,5)).set_index(0)
In[26]: df
Out[26]:
1 2 3 4
0
0 1 2 3 4
5 6 7 8 9
10 11 12 13 14
15 16 17 18 19
20 21 22 23 24
In[27]: df.loc[[10,15],[2,3,4]] = "x"
In[28]: df
Out[28]:
1 2 3 4
0
0 1 2 3 4
5 6 7 8 9
10 11 x x x
15 16 x x x
20 21 22 23 24
To do that, just set the column 0 as index, then select the portion of the dataframe with loc and assign the value "x".
It works in the same way for your last dataset:
In[51]: Modify_List_col = ['mary_02', 'peter_03']
Modify_List_row = ['2nd']
df.loc[Modify_List_row, Modify_List_col] = "X"
In[52]: df
Out[52]:
john_01 ken_05 mary_02 peter_03 roger_04
name
1st 1 13 4 7 10
2nd 2 14 X X 11
3rd 3 15 6 9 12
I hope this can help you.
I have a csv file that shows parts on order. The columns include days late, qty and commodity.
I need to group the data by days late and commodity with a sum of the qty. However the days late needs to be grouped into ranges.
>56
>35 and <= 56
>14 and <= 35
>0 and <=14
I was hoping I could use a dict some how. Something like this
{'Red':'>56,'Amber':'>35 and <= 56','Yellow':'>14 and <= 35','White':'>0 and <=14'}
I am looking for a result like this
Red Amber Yellow White
STRSUB 56 60 74 40
BOTDWG 20 67 87 34
I am new to pandas so I don't know if this is possible at all. Could anyone provide some advice.
Thanks
Suppose you start with this data:
df = pd.DataFrame({'ID': ('STRSUB BOTDWG'.split())*4,
'Days Late': [60, 60, 50, 50, 20, 20, 10, 10],
'quantity': [56, 20, 60, 67, 74, 87, 40, 34]})
# Days Late ID quantity
# 0 60 STRSUB 56
# 1 60 BOTDWG 20
# 2 50 STRSUB 60
# 3 50 BOTDWG 67
# 4 20 STRSUB 74
# 5 20 BOTDWG 87
# 6 10 STRSUB 40
# 7 10 BOTDWG 34
Then you can find the status category using pd.cut. Note that by default, pd.cut splits the Series df['Days Late'] into categories which are half-open intervals, (-1, 14], (14, 35], (35, 56], (56, 365]:
df['status'] = pd.cut(df['Days Late'], bins=[-1, 14, 35, 56, 365], labels=False)
labels = np.array('White Yellow Amber Red'.split())
df['status'] = labels[df['status']]
del df['Days Late']
print(df)
# ID quantity status
# 0 STRSUB 56 Red
# 1 BOTDWG 20 Red
# 2 STRSUB 60 Amber
# 3 BOTDWG 67 Amber
# 4 STRSUB 74 Yellow
# 5 BOTDWG 87 Yellow
# 6 STRSUB 40 White
# 7 BOTDWG 34 White
Now use pivot to get the DataFrame in the desired form:
df = df.pivot(index='ID', columns='status', values='quantity')
and use reindex to obtain the desired order for the rows and columns:
df = df.reindex(columns=labels[::-1], index=df.index[::-1])
Thus,
import numpy as np
import pandas as pd
df = pd.DataFrame({'ID': ('STRSUB BOTDWG'.split())*4,
'Days Late': [60, 60, 50, 50, 20, 20, 10, 10],
'quantity': [56, 20, 60, 67, 74, 87, 40, 34]})
df['status'] = pd.cut(df['Days Late'], bins=[-1, 14, 35, 56, 365], labels=False)
labels = np.array('White Yellow Amber Red'.split())
df['status'] = labels[df['status']]
del df['Days Late']
df = df.pivot(index='ID', columns='status', values='quantity')
df = df.reindex(columns=labels[::-1], index=df.index[::-1])
print(df)
yields
Red Amber Yellow White
ID
STRSUB 56 60 74 40
BOTDWG 20 67 87 34
You can create a column in your DataFrame based on your Days Late column by using the map or apply functions as follows. Let's first create some sample data.
df = pandas.DataFrame({ 'ID': 'foo,bar,foo,bar,foo,bar,foo,foo'.split(','),
'Days Late': numpy.random.randn(8)*20+30})
Days Late ID
0 30.746244 foo
1 16.234267 bar
2 14.771567 foo
3 33.211626 bar
4 3.497118 foo
5 52.482879 bar
6 11.695231 foo
7 47.350269 foo
Create a helper function to transform the data of the Days Late column and add a column called Code.
def days_late_xform(dl):
if dl > 56: return 'Red'
elif 35 < dl <= 56: return 'Amber'
elif 14 < dl <= 35: return 'Yellow'
elif 0 < dl <= 14: return 'White'
else: return 'None'
df["Code"] = df['Days Late'].map(days_late_xform)
Days Late ID Code
0 30.746244 foo Yellow
1 16.234267 bar Yellow
2 14.771567 foo Yellow
3 33.211626 bar Yellow
4 3.497118 foo White
5 52.482879 bar Amber
6 11.695231 foo White
7 47.350269 foo Amber
Lastly, you can use groupby to aggregate by the ID and Code columns, and get the counts of the groups as follows:
g = df.groupby(["ID","Code"]).size()
print g
ID Code
bar Amber 1
Yellow 2
foo Amber 1
White 2
Yellow 2
df2 = g.unstack()
print df2
Code Amber White Yellow
ID
bar 1 NaN 2
foo 1 2 2
I know this is coming a bit late, but I had the same problem as you and wanted to share the function np.digitize. It sounds like exactly what you want.
a = np.random.randint(0, 100, 50)
grps = np.arange(0, 100, 10)
grps2 = [1, 20, 25, 40]
print a
[35 76 83 62 57 50 24 0 14 40 21 3 45 30 79 32 29 80 90 38 2 77 50 73 51
71 29 53 76 16 93 46 14 32 44 77 24 95 48 23 26 49 32 15 2 33 17 88 26 17]
print np.digitize(a, grps)
[ 4 8 9 7 6 6 3 1 2 5 3 1 5 4 8 4 3 9 10 4 1 8 6 8 6
8 3 6 8 2 10 5 2 4 5 8 3 10 5 3 3 5 4 2 1 4 2 9 3 2]
print np.digitize(a, grps2)
[3 4 4 4 4 4 2 0 1 4 2 1 4 3 4 3 3 4 4 3 1 4 4 4 4 4 3 4 4 1 4 4 1 3 4 4 2
4 4 2 3 4 3 1 1 3 1 4 3 1]