I have a pandas dataframe df
Date SKU Balance
0 1/1/2017 X1 8
1 2/1/2017 X2 45
2 3/1/2017 X1 47
3 4/1/2017 X2 16
4 5/1/2017 X1 14
5 6/1/2017 X2 67
6 7/1/2017 X2 9
8 8/1/2017 X1 66
9 9/1/2017 X1 158
I wanna break it and append it to a list so that each item in the list is the collection of 4 days of the data frame
For Example
List[1]
Date SKU Balance
0 1/1/2017 X1 8
1 2/1/2017 X2 45
2 3/1/2017 X1 47
3 4/1/2017 X2 16
List[2]
Date SKU Balance
0 2/1/2017 X2 45
1 3/1/2017 X1 47
2 4/1/2017 X2 16
3 5/1/2017 X1 14
At the moment I can only achieve by appending one day for each list by this below code
dr = pd.date_range('20170101','20170109')
list=[]
for d in dr:
list.append(df.loc[df.Date.isin([d])])
As mentioned above,How can I append 4 days from the 1st day in one list and loop it to the 2nd day , append another 4 days of rows and so on.
Highly appreciate your help
Use reindex and np.r_ with list comprehension:
l = [df.reindex(np.r_[i:i+4]) for i in range(len(df))]
You can try with np.roll
l=[]
a=df.index.values
for x in a:
l.append(df.loc[a[:4]])
a=np.roll(a,-1)
Slice in a list comprehension.
ls = [df.loc[i:i+3] for i in range(len(df))]
Related
I have a function to apply to this table
F(x) = 1.5*x1 + 2*x2 - 1.5*x3
Where xi, i = 1,2,3, is the column value.
And I have the following table below.
X1 | X2 | X3
------|------|------
20 |15 |12
30 |17 |24
40 |23 |36
The desired output that I would like is the following below, where I apply the function to each row, taking the value in each column and applying it to the function iteratively then receiving value as a sum and another column appended onto the dataframe.
X1 | X2 | X3 |F(X)
------|------|------|------
20 |15 |12 |42
30 |17 |24 |43
40 |23 |36 |52
Is there a way to do this in Python 2.7?
Something like this ?
df['F(x)']=df.mul([1.5,2,-1.5]).sum(1)
df
Out[1076]:
X1 X2 X3 F(x)
0 20 15 12 42.0
1 30 17 24 43.0
2 40 23 36 52.0
Ok. Found a sample code to solve my problem.
var1 = 1.5
var2 = 2
var3 = -1.5
def calculate_fx(row):
return (var1 * row['X1']) + (var2 * row['X2']) + (var3 * row['X3'])
#function_df is the predefined dataframe
function_df['F(X)'] = function_df.apply(calculate_fx, axis=1)
function_df
I have to compare a columns with all other columns in the dataframe. The column that i have to compare with others is located in position 4 so i write df.iloc[x,4] to take column values. Then i have to consider these values, multiply them with the values in the next column (for example df.iloc[x,5]), create a new column in the dataframe and save results. Then i have to repeat this procedure to the end the existing column (the original dataframe has 43 column, so the end it is the df.iloc[x,43] )
How can i do this in python?
If it is possibile can you do some examples? I try to put my code in the post but i 'm not good with my new phone.
I think you can use eq - compare filtered DataFrame with column E in position 4:
df = pd.DataFrame({'A':[1,2,3],
'B':[4,5,6],
'C':[7,8,9],
'D':[1,3,5],
'E':[5,3,6],
'F':[7,8,9],
'G':[1,3,5],
'H':[5,3,6],
'I':[7,4,3]})
print (df)
A B C D E F G H I
0 1 4 7 1 5 7 1 5 7
1 2 5 8 3 3 8 3 3 4
2 3 6 9 5 6 9 5 6 3
print (df.iloc[:,5:].eq(df.iloc[:,4], axis=0))
F G H I
0 False False True False
1 False True True False
2 False False True False
If need multiple by column in position 4 use mul:
print (df.iloc[:,5:].mul(df.iloc[:,4], axis=0))
F G H I
0 35 5 25 35
1 24 9 9 12
2 54 30 36 18
Or if need multiple by shifted columns:
print (df.iloc[:,4:].mul(df.iloc[:,5:], axis=0, fill_value=1))
E F G H I
0 5.0 49 1 25 49
1 3.0 64 9 9 16
2 6.0 81 25 36 9
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
I have a dataset that has hundreds of thousands of fields. The following is a simplified dataset
dataSet <- c("Plnt SLoc Material Description L.T MRP Stat Auto MatSG PC PN Freq Qty CFreq CQty Cur.RPt New.RPt CurRepl NewRepl Updt Cost ServStock Unit OpenMatResb DFStorLocLevel",
"0231 0002 GB.C152260-00001 ASSY PISTON & SEAL/O-RING 44 PD X A A A 18 136 30 29 50 43 24.88 51.000 EA",
"0231 0002 WH.112734 MOTOR REDUCER, THREE-PHAS 41 PD X B B A 16 17 3 3 5 4 483.87 1.000 EA X",
"0231 0002 WH.920569 SPINDLE MOTOR MINI O 22 PD X A A A 69 85 15 9 25 13 680.91 21.000 EA",
"0231 0002 GB.C150583-00001 VALVE-AIR MDI 64 PD X A A A 16 113 50 35 80 52 19.96 116.000 EA",
"0231 0002 FG.124-0140 BEARING 32 PD X A A A 36 205 35 32 50 48 21.16 55.000 EA",
"0231 0002 WP.254997 BEARING,BALL .9843 X 2.04 52 PD X A A A 18 155 50 39 100 58 2.69 181.000 EA"
)
I would like to create a dataframe out of this dataSet for further calculation. The approach I am following is as follows:
I split the dataSet by space and then recombine it.
dataSetSplit <- strsplit(dataSet, "\\s+")
The header (which is the first line) splits correctly and produces 25 characters. This can be seen by the str() function.
str(dataSetSplit)
I will then intend to combine all the rows together using the folloing script
combinedData <- data.frame(do.call(rbind, dataSetSplit))
Please note that the above script "combinedData " errors because the split did not produce equal number of fields.
For this approach to work all the fields must split correctly into 25 fields.
If you think this is a sound approach please let me know how to split the fileds into 25 fields.
It is worth mentioning that I do not like the approach of splitting the data set with the function strsplit(). It is an extremely time consuming step if used with a large data set. Can you please recommend an alternate approach to create a data frame out of the supplied data?
By the looks of it, you have a header row that is actually helpful. You can easily use gregexpr to calculate your "widths" to use with read.fwf.
Here's how:
## Use gregexpr to find the position of consecutive runs of spaces
## This will tell you the starting position of each column
Widths <- gregexpr("\\s+", dataSet[1])[[1]]
## `read.fwf` doesn't need the starting position, but the width of
## each column. We can use `diff` to calculate this.
Widths <- c(Widths[1], diff(Widths))
## Since there are no spaces after the last column, we need to calculate
## a reasonable width for that column too. We can do this with `nchar`
## to find the widest row in the data. From this, subtract the `sum`
## of all the previous values.
Widths <- c(Widths, max(nchar(dataSet)) - sum(Widths))
Let's also extract the column names. We could do this in read.fwf, but it would require us to substitute the spaces in the first line with a "sep" character.
Names <- scan(what = "", text = dataSet[1])
Now, read in everything except the first line. You would use the actual file instead of textConnection, I would suppose.
read.fwf(textConnection(dataSet), widths=Widths, strip.white = TRUE,
skip = 1, col.names = Names)
# Plnt SLoc Material Description L.T MRP Stat Auto MatSG PC PN Freq Qty
# 1 231 2 GB.C152260-00001 ASSY PISTON & SEAL/O-RING 44 PD NA X A A A 18 136
# 2 231 2 WH.112734 MOTOR REDUCER, THREE-PHAS 41 PD NA X B B A 16 17
# 3 231 2 WH.920569 SPINDLE MOTOR MINI O 22 PD NA X A A A 69 85
# 4 231 2 GB.C150583-00001 VALVE-AIR MDI 64 PD NA X A A A 16 113
# 5 231 2 FG.124-0140 BEARING 32 PD NA X A A A 36 205
# 6 231 2 WP.254997 BEARING,BALL .9843 X 2.04 52 PD NA X A A A 18 155
# CFreq CQty Cur.RPt New.RPt CurRepl NewRepl Updt Cost ServStock Unit OpenMatResb
# 1 NA NA 30 29 50 43 NA 24.88 51 EA <NA>
# 2 NA NA 3 3 5 4 NA 483.87 1 EA X
# 3 NA NA 15 9 25 13 NA 680.91 21 EA <NA>
# 4 NA NA 50 35 80 52 NA 19.96 116 EA <NA>
# 5 NA NA 35 32 50 48 NA 21.16 55 EA <NA>
# 6 NA NA 50 39 100 58 NA 2.69 181 EA <NA>
# DFStorLocLevel
# 1 NA
# 2 NA
# 3 NA
# 4 NA
# 5 NA
# 6 NA
Many thanks to Ananda Mahto, he provided many pieces to this answer.
widthMinusFirst <- diff(gregexpr('(\\s[A-Z])+', dataSet[1])[[1]])
widthFirst <- gregexpr('\\s+', dataSet[1])[[1]][1]
Width <- c(widthFirst, widthMinusFirst)
Widths <- c(Width, max(nchar(dataSet)) - sum(Width))
columnNames <- scan(what = "", text = dataSet[1])
read.fwf(textConnection(dataSet[-1]), widths = Widths, strip.white = FALSE,
skip = 0, col.names = columnNames)
I have a list of comprising of sub-lists with different numbers of entries, as follows:
x <- list(
c("a1", "a2", "a3", "a4", "a5", "a6", "a7"),
c("b1","b2","b3","b4"),
c("c1","c2","c3"),
c("d1")
)
I want to convert this file to a dataframe with three columns (1st column is sequence of the sub-list, i.e. 1 to 4: 2nd column is the entries; the 3rd stands for my stop code, so, I used 1 for every lines, the final results is as follows:
1 a1 1
1 a2 1
1 a3 1
1 a4 1
1 a5 1
1 a6 1
1 a7 1
2 b1 1
2 b2 1
2 b3 1
2 b4 1
3 c1 1
3 c2 1
3 c3 1
4 d1 1
I tried to use cbind, however, it seems to me only works for sub-list with same number of entries. Are there any smarter way of doing this?
here is an example:
data.frame(
x=rep(1:length(x), sapply(x, length)),
y=unlist(x),
z=1
)
library(reshape2)
x <- melt(x) ## Done...
## Trivial...
x$stop <- 1
x <- x[c(2,1,3)]
One option is to use the split, apply, combine functionality in package plyr. In this case you need ldply which will take a list and combine the elements into data.frame:
library(plyr)
ldply(seq_along(x), function(i)data.frame(n=i, x=x[[i]], stop=1))
n x stop
1 1 a1 1
2 1 a2 1
3 1 a3 1
4 1 a4 1
5 1 a5 1
6 1 a6 1
7 1 a7 1
8 2 b1 1
9 2 b2 1
10 2 b3 1
11 2 b4 1
12 3 c1 1
13 3 c2 1
14 3 c3 1
15 4 d1 1