How to find out if there was weekend between days? - python

I have two data frames. One representing when an order was placed and arrived, while the other one represents the working days of the shop.
Days are taken as days of the year. i.e. 32 = 1th February.
orders = DataFrame({'placed':[100,103,104,105,108,109], 'arrived':[103,104,105,106,111,111]})
Out[25]:
arrived placed
0 103 100
1 104 103
2 105 104
3 106 105
4 111 108
5 111 109
calendar = DataFrame({'day':['100','101','102','103','104','105','106','107','108','109','110','111','112','113','114','115','116','117','118','119','120'], 'closed':[0,1,1,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0]})
Out[21]:
closed day
0 0 100
1 1 101
2 1 102
3 0 103
4 0 104
5 0 105
6 0 106
7 0 107
8 1 108
9 1 109
10 0 110
11 0 111
12 0 112
13 0 113
14 0 114
15 1 115
16 1 116
17 0 117
18 0 118
19 0 119
20 0 120
What i want to do is to compute the difference between placed and arrived
x = orders['arrived'] - orders['placed']
Out[24]:
0 3
1 1
2 1
3 1
4 3
5 2
dtype: int64
and subtract one if any day between arrived and placed (included) was a day in which the shop was closed.
i.e. in the first row the order is placed on day 100 and arrived on day 103. the day used are 100, 101, 102, 103. the difference between 103 and 100 is 3. However, since 101 and 102 are days in which the shop is closed I want to subtract 1 for each. That is 3 -1 -1 = 1. And finally append this result on the orders df.

Related

Loop through dataframe in python to select specific row

I have a timeseries data of 5864 ICU Patients and my dataframe is like this. Each row is the ICU stay of respective patient at a particular hour.
HR
SBP
DBP
ICULOS
Sepsis
P_ID
92
120
80
1
0
0
98
115
85
2
0
0
93
125
75
3
1
0
95
130
90
4
1
0
102
120
80
1
0
1
109
115
75
2
0
1
94
135
100
3
0
1
97
100
70
4
1
1
85
120
80
5
1
1
88
115
75
6
1
1
93
125
85
1
0
2
78
130
90
2
0
2
115
140
110
3
0
2
102
120
80
4
0
2
98
140
110
5
1
2
I want to select the ICULOS where Sepsis = 1 (first hour only) based on patient ID. Like in P_ID = 0, Sepsis = 1 at ICULOS = 3. I did this on a single patient (the dataframe having data of only a single patient) using the code:
x = df[df['Sepsis'] == 1]["ICULOS"].values[0]
print("ICULOS at which Sepsis Label = 1 is:", x)
# Output
ICULOS at which Sepsis Label = 1 is: 46
If I want to check it for each P_ID, I have to do this 5864 times. Can someone help me with the code using a loop? The loop will go to each P_ID and then give the result of ICULOS where Sepsis = 1. Looking forward for help.

for x in df['P_ID'].unique():
print(df.query('P_ID == #x and Sepsis == 1')['ICULOS'][0])

First, filter the rows which have Sepsis=1. It will automatically filter the P_IDs which don't have Sepsis as 1. Thus, you will have fewer patients to iterate.
df1 = df[df.Sepsis==1]
for pid in df.P_ID.unique():
if pid not in df.P_ID:
print("P_ID: {pid} - it has no iclus at Sepsis Lable = 1")
else:
iclus = df1[df1.P_ID==pid].ICULOS.values[0]
print(f"P_ID: {pid} - ICULOS at which Sepsis Label = 1 is: {iclus}")

Average of every x rows with a step size of y per each subset using pandas

I have a pandas data frame like this:
Subset Position Value
1 1 2
1 10 3
1 15 0.285714
1 43 1
1 48 0
1 89 2
1 132 2
1 152 0.285714
1 189 0.133333
1 200 0
2 1 0.133333
2 10 0
2 15 2
2 33 2
2 36 0.285714
2 72 2
2 132 0.133333
2 152 0.133333
2 220 3
2 250 8
2 350 6
2 750 0
I want to know how can I get the mean of values for every "x" row with "y" step size per subset in pandas?
For example, mean of every 5 rows (step size =2) for value column in each subset like this:
Subset Start_position End_position Mean
1 1 48 1.2571428
1 15 132 1.0571428
1 48 189 0.8838094
2 1 36 0.8838094
2 15 132 1.2838094
2 36 220 1.110476
2 132 350 3.4533332

Is this what you were looking for:
df = pd.DataFrame({'Subset': [1]*10+[2]*12,
'Position': [1,10,15,43,48,89,132,152,189,200,1,10,15,33,36,72,132,152,220,250,350,750],
'Value': [2,3,.285714,1,0,2,2,.285714,.1333333,0,0.133333,0,2,2,.285714,2,.133333,.133333,3,8,6,0]})
averaged_df = pd.DataFrame(columns=['Subset', 'Start_position', 'End_position', 'Mean'])
window = 5
step_size = 2
for subset in df.Subset.unique():
subset_df = df[df.Subset==subset].reset_index(drop=True)
for i in range(0,len(df),step_size):
window_rows = subset_df.iloc[i:i+window]
if len(window_rows) < window:
continue
window_average = {'Subset': window_rows.Subset.loc[0+i],
'Start_position': window_rows.Position[0+i],
'End_position': window_rows.Position.iloc[-1],
'Mean': window_rows.Value.mean()}
averaged_df = averaged_df.append(window_average,ignore_index=True)
Some notes about the code:
It assumes all subsets are in order in the original df (1,1,2,1,2,2 will behave as if it was 1,1,1,2,2,2)
If there is a group left that's smaller than a window, it will skip it (e.g. 1, 132, 200, 0,60476 is not included`)

One version specific answer would be, using pandas.api.indexers.FixedForwardWindowIndexer introduced in pandas 1.1.0:
>>> window=5
>>> step=2
>>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=window)
>>> df2 = df.join(df.Position.shift(-(window-1)), lsuffix='_start', rsuffix='_end')
>>> df2 = df2.assign(Mean=df2.pop('Value').rolling(window=indexer).mean()).iloc[::step]
>>> df2 = df2[df2.Position_start.lt(df2.Position_end)].dropna()
>>> df2['Position_end'] = df2['Position_end'].astype(int)
>>> df2
Subset Position_start Position_end Mean
0 1 1 48 1.257143
2 1 15 132 1.057143
4 1 48 189 0.883809
10 2 1 36 0.883809
12 2 15 132 1.283809
14 2 36 220 1.110476
16 2 132 350 3.453333

find the maximum value for each streak of numbers in another column in pandas

I have a dataframe like this :
df = pd.DataFrame({'dir': [1,1,1,1,0,0,1,1,1,0], 'price':np.random.randint(100,200,10)})
dir price
0 1 100
1 1 150
2 1 190
3 1 194
4 0 152
5 0 151
6 1 131
7 1 168
8 1 112
9 0 193
and I want a new column that shows the maximum price as long as the dir is 1 and reset if dir is 0.
My desired outcome looks like this:
dir price max
0 1 100 194
1 1 150 194
2 1 190 194
3 1 194 194
4 0 152 NaN
5 0 151 NaN
6 1 131 168
7 1 168 168
8 1 112 168
9 0 193 NaN

Use transform with max for filtered rows:
#get unique groups for consecutive values
g = df['dir'].ne(df['dir'].shift()).cumsum()
#filter only 1
m = df['dir'] == 1
df['max'] = df[m].groupby(g)['price'].transform('max')
print (df)
dir price max
0 1 100 194.0
1 1 150 194.0
2 1 190 194.0
3 1 194 194.0
4 0 152 NaN
5 0 151 NaN
6 1 131 168.0
7 1 168 168.0
8 1 112 168.0
9 0 193 NaN

selecting a column from pandas pivot table

I have the below pivot table which I created from a dataframe using the following code:
table = pd.pivot_table(df, values='count', index=['days'],columns['movements'], aggfunc=np.sum)
movements 0 1 2 3 4 5 6 7
days
0 2777 51 2
1 6279 200 7 3
2 5609 110 32 4
3 4109 118 101 8 3
4 3034 129 109 6 2 2
5 2288 131 131 9 2 1
6 1918 139 109 13 1 1
7 1442 109 153 13 10 1
8 1085 76 111 13 7 1
9 845 81 86 8 8
10 646 70 83 1 2 1 1
As you can see from pivot table that it has 8 columns from 0-7 and now I want to plot some specific columns instead of all. I could not manage to select columns. Lets say I want to plot column 0 and column 2 against index. what should I use for y to select column 0 and column 2?
plt.plot(x=table.index, y=??)
I tried with y = table.value['0', '2'] and y=table['0','2'] but nothing works.

You cannot select ndarray for y if you need those two column values in a single plot you can use:
plt.plot(table['0'])
plt.plot(table['2'])
If column names are intergers then:
plt.plot(table[0])
plt.plot(table[2])

Dropping certain values in columns depending on contents of other column

I have a dataframe that looks like this:
Deal Year Financial Data1 Financial Data2 Financial Data3 Quarter
0 1 1991/1/1 122 123 120 1
3 1 1991/1/1 122 123 120 2
6 1 1991/1/1 122 123 120 3
1 2 1992/1/1 85 90 80 4
4 2 1992/1/1 85 90 80 5
7 2 1992/1/1 85 90 80 6
2 3 1993/1/1 85 90 100 1
5 3 1993/1/1 85 90 100 2
8 3 1993/1/1 85 90 100 3
However I only want the Financial Data1 displayed for the first quarter in each deal and The whole thing combined into one column again.
The end result should look something like this:
Deal Year Financial Data Quarter
0 1 1991/1/1 122 1
3 1 1991/1/1 123 2
6 1 1991/1/1 120 3
1 2 1992/1/1 85 4
4 2 1992/1/1 90 5
7 2 1992/1/1 80 6
2 3 1993/1/1 85 1
5 3 1993/1/1 90 2
8 3 1993/1/1 100 3

Okie dokie, using np.where() I think this does what you're trying to do:
import pandas as pd
import numpy as np
df = pd.read_fwf(StringIO(
"""Deal Year Financial_Data1 Financial_Data2 Financial_Data3 Quarter
1 1991/1/1 122 123 120 1
1 1991/1/1 122 123 120 2
1 1991/1/1 122 123 120 3
2 1992/1/1 85 90 80 4
2 1992/1/1 85 90 80 5
2 1992/1/1 85 90 80 6
3 1993/1/1 85 90 100 1
3 1993/1/1 85 90 100 2
3 1993/1/1 85 90 100 3"""))
df['Financial_Data'] = np.where(
# if 'Quarter'%3==1
df['Quarter']%3==1,
# Then return Financial_Data1
df['Financial_Data1'],
# Else
np.where(
# If 'Quarter'%3==2
df['Quarter']%3==2,
# Then return Financial_Data2
df['Financial_Data2'],
# Else return Financial_Data3
df['Financial_Data3']
)
)
# Drop Old Columns
df = df.drop(['Financial_Data1', 'Financial_Data2', 'Financial_Data3'], axis=1)
print(df)
Output:
Deal Year Quarter Financial_Data
0 1 1991/1/1 1 122
1 1 1991/1/1 2 123
2 1 1991/1/1 3 120
3 2 1992/1/1 4 85
4 2 1992/1/1 5 90
5 2 1992/1/1 6 80
6 3 1993/1/1 1 85
7 3 1993/1/1 2 90
8 3 1993/1/1 3 100
(PS: I wasn't 100% sure how you intended on dealing with Quarter 4-6, in this example I just treat them as 1-3)

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