Have got a dataframe like below:
Store Row_no
11 56
11 57
11 58
12 89
12 90
12 91
12 92
For each store need to get 3rd minimum value from Row_no. Expected output below.
Store Row_no
11 58
12 91
have tried df.Row_no.nsmallest(3) but it works different. Any help will be appreciated. Thank You!
Use DataFrame.sort_values with GroupBy.nth:
df = df.sort_values(['Store','Row_no']).groupby('Store', as_index=False).nth(2)
print (df)
Store Row_no
2 11 58
5 12 91
Related
I have a data frame with numbers in multiple columns listed by date, what I'm trying to do is find out the most frequently occurring numbers across the whole data set, also grouped by date.
import pandas as pd
import glob
def lotnorm(pdobject) :
# clean up special characters in the column names and make the date column the index as a date type.
pdobject["Date"] = pd.to_datetime(pdobject["Date"])
pdobject = pdobject.set_index('Date')
for column in pdobject:
if '#' in column:
pdobject = pdobject.rename(columns={column:column.replace('#','')})
return pdobject
def lotimport() :
lotret = {}
# list files in data directory with csv filename
for lotpath in [f for f in glob.glob("data/*.csv")]:
lotname = lotpath.split('\\')[1].split('.')[0]
lotret[lotname] = lotnorm(pd.read_csv(lotpath))
return lotret
print(lotimport()['ozlotto'])
------------- Output ---------------------
1 2 3 4 5 6 7 8 9
Date
2020-07-07 4 5 7 9 12 13 32 19 35
2020-06-30 1 17 26 28 38 39 44 14 41
2020-06-23 1 3 9 13 17 20 41 28 45
2020-06-16 1 2 13 21 22 27 38 24 33
2020-06-09 8 11 26 27 31 38 39 3 36
... .. .. .. .. .. .. .. .. ..
2005-11-15 7 10 13 17 30 32 41 20 14
2005-11-08 12 18 22 28 33 43 45 23 13
2005-11-01 1 3 11 17 24 34 43 39 4
2005-10-25 7 16 23 29 36 39 42 19 43
2005-10-18 5 9 12 30 33 39 45 7 19
The output I am aiming for is
Number frequency
45 201
32 195
24 187
14 160
48 154
--------------- Updated with append experiment -----------
I tried using append to create a single series from the dataframe, which worked for individual lines of code but got a really odd result when I ran it inside a for loop.
temp = lotimport()['ozlotto']['1']
print(temp)
temp = temp.append(lotimport()['ozlotto']['2'], ignore_index=True, verify_integrity=True)
print(temp)
temp = temp.append(lotimport()['ozlotto']['3'], ignore_index=True, verify_integrity=True)
print(temp)
lotcomb = pd.DataFrame()
for i in (lotimport()['ozlotto'].columns.tolist()):
print(f"{i} - {type(i)}")
lotcomb = lotcomb.append(lotimport()['ozlotto'][i], ignore_index=True, verify_integrity=True)
print(lotcomb)
This solution might be the one you are looking for.
freqvalues = np.unique(df.to_numpy(), return_counts=True)
df2 = pd.DataFrame(index=freqvalues[0], data=freqvalues[1], columns=["Frequency"])
df2.index.name = "Numbers"
df2
Output:
Frequency
Numbers
1 6
2 5
3 5
5 8
6 4
7 7
8 2
9 7
10 3
11 4
12 2
13 8
14 1
15 4
16 4
17 6
18 4
19 5
20 9
21 3
22 4
23 2
24 4
25 5
26 4
27 6
28 1
29 6
30 3
31 3
... ...
70 6
71 6
72 5
73 5
74 2
75 8
76 5
77 3
78 3
79 2
80 3
81 4
82 6
83 9
84 5
85 4
86 1
87 3
88 4
89 3
90 4
91 4
92 3
93 5
94 1
95 4
96 6
97 6
98 1
99 6
97 rows × 1 columns
df.max(axis=0)
for columns
df.max(axis=1)
for index
Ok so the final answer I came up with was a mix of a few things including some of the great input from people in this thread. Essentially I do the following:
Pull in the CSV file and clean up the dates and the column names, then convert it to a pandas dataframe.
Then create a new pandas series and append each column to it ignoring dates to prevent conflicts.
Once I have the series, I use Vioxini's suggestion to use numpy to get counts of unique values and then turn the values into the index, after that sort the column by count in descending order and return the top 10 values.
Below is the resulting code, I hope it helps someone else.
import pandas as pd
import glob
import numpy as np
def lotnorm(pdobject) :
# clean up special characters in the column names and make the date column the index as a date type.
pdobject["Date"] = pd.to_datetime(pdobject["Date"])
pdobject = pdobject.set_index('Date')
for column in pdobject:
if '#' in column:
pdobject = pdobject.rename(columns={column:column.replace('#','')})
return pdobject
def lotimport() :
lotret = {}
# list files in data directory with csv filename
for lotpath in [f for f in glob.glob("data/*.csv")]:
lotname = lotpath.split('\\')[1].split('.')[0]
lotret[lotname] = lotnorm(pd.read_csv(lotpath))
return lotret
lotcomb = pd.Series([],dtype=object)
for i in (lotimport()['ozlotto'].columns.tolist()):
lotcomb = lotcomb.append(lotimport()['ozlotto'][i], ignore_index=True, verify_integrity=True)
freqvalues = np.unique(lotcomb.to_numpy(), return_counts=True)
lotop = pd.DataFrame(index=freqvalues[0], data=freqvalues[1], columns=["Frequency"])
lotop.index.name = "Numbers"
lotop.sort_values(by=['Frequency'],ascending=False).head(10)
How can I reference the minimum value of two dataframes as part of a pandas dataframe equation? I tried using the python min() function which did not work. I'm sorry if this is well-documented somewhere but I have not been able to find a working solution for this problem. I am looking for something along the lines of this:
data['eff'] = pd.DataFrame([data['flow_h'], data['flow_c']]).min() *Cp* (data[' Thi'] - data[' Tci'])
I also tried to use pandas min() function, which is also not working.
min_flow = pd.DataFrame([data['flow_h'], data['flow_c']]).min()
InvalidIndexError: Reindexing only valid with uniquely valued Index objects
I was confused by this error. The data columns are just numbers and a name, I wasn't sure where the index comes into play.
import pandas as pd
import numpy as np
np.random.seed(365)
rows = 10
flow = {'flow_c': [np.random.randint(100) for _ in range(rows)],
'flow_d': [np.random.randint(100) for _ in range(rows)],
'flow_h': [np.random.randint(100) for _ in range(rows)]}
data = pd.DataFrame(flow)
# display(data)
flow_c flow_d flow_h
0 82 36 43
1 52 48 12
2 33 28 77
3 91 99 11
4 44 95 27
5 5 94 64
6 98 3 88
7 73 39 92
8 26 39 62
9 56 74 50
If you are trying to get the row-wise mininum of two or more columns, use pandas.DataFrame.min. Note that by default axis=0; specifying axis=1 is necessary.
data['min_c_h'] = data[['flow_h','flow_c']].min(axis=1)
# display(data)
flow_c flow_d flow_h min_c_h
0 82 36 43 43
1 52 48 12 12
2 33 28 77 33
3 91 99 11 11
4 44 95 27 27
5 5 94 64 5
6 98 3 88 88
7 73 39 92 73
8 26 39 62 26
9 56 74 50 50
If you like to get a single minimum value of multiple columns:
data[['flow_h','flow_c']].min().min()
the first "min()" calculates the minimum per column and returns a pandas series. The second "min" returns the minimum of the minimums per column.
So here I have 2 tables.
ring layer1 layer2 output
12 45 46 bingo
12 34 75
13 23 47
14 23 34 nice_work
14 12 15
14 45 23
14 67 89 wow
25 90 124
67 76 341
ring whole_string value as output
12 23_45_12_78_46 bingo
12 78_89_23_45_90 great
13 23_89_90 awesome
14 45_78_23_45_34 nice_work
14 88_86_85_12 cool
14 67_89_111 wow
what I need is: value as output from tbl2 if
1. tbl1 ring = tbl2 ring
2. tbl1 layer1 & layer2 values must be present in tbl2 whole_string
Can someone help me with excel formula?
Thank you...
I tried using a for loop. It takes a whole lot time.
You could use:
Formula in D2:
=IFERROR(INDEX($H$1:$H$7,AGGREGATE(14,3,($F$2:$F$7=A2)*(IF(ISNUMBER(SEARCH("_"&B2&"_","_"&$G$2:$G$7&"_")),1,""))*(IF(ISNUMBER(SEARCH("_"&C2&"_","_"&$G$2:$G$7&"_")),1,""))*ROW($F$2:$F$7),1)),"")
Entered as array formula through: Ctrl+Shift+Enter
Drag down...
That is my data set enter code here
Year created Week created SUM_New SUM_Closed SUM_Open
0 2018 1 17 0 82
1 2018 6 62 47 18
2 2018 6 62 47 18
3 2018 6 62 47 18
4 2018 6 62 47 18
In last three columns there is already the sum for the year and week. I need to get rid of duplicates so that the table contains unique values (for the example above):
Year created Week created SUM_New SUM_Closed SUM_Open
0 2018 1 17 0 82
4 2018 6 62 47 18
I tried to group data but it somehow works wrong and does what I need but just for one column.
df.groupby(['Year created', 'Week created']).size()
And output:
Year created Week created
2017 48 2
49 25
50 54
51 36
52 1
2018 1 17
2 50
3 37
But it is just one column and I don't know which one because even if I separate the data on three parts and do the same procedure for each part I get the same result (as above) for all.
I believe need drop_duplicates:
df = df.drop_duplicates(['Year created', 'Week created'])
print (df)
Year created Week created SUM_New SUM_Closed SUM_Open
0 2018 1 17 0 82
1 2018 6 62 47 18
df2 = df.drop_duplicates(['Year created', 'Week created', 'SUM_New', 'SUM_Closed'])
print(df2)
hope this helps.
I have ProductID:
ProductID
12
85
14
14
14
17
65
65
I need to create a new column where i could count how many times i have the same ProductID. I mean:
ProductID Count
12 1
85 1
14 1
14 2
14 3
17 1
65 1
65 2
The formula I currently have is = IF((A3=A2),F2+1,1), but it doesn't work.
Use this formula if you want the count of each individual entry to be same. (i.e count for 14 will be 3 as it exists three times in the data set)
=COUNTIF($A$2:A9,A2)
Output is attached for your reference.
In B2 enter:
=COUNTIF($A$1:A2,A2)
and copy down