I am creating dicts where the dict keys are strings and the values are large-ish pandas DataFrames. I would like to write these dicts to an excel file but the issue I'm having is that when python writes the dataframe to a csv it cuts out parts. Code:
import pandas as pd
import numpy as np
def create_random_df():
return(pd.DataFrame(np.random.randint(0,100,size=(70,26)),columns=list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')))
dic={'key1': create_random_df() , 'key2': create_random_df()}
with open('test.csv', 'w') as f:
for key in dic.keys():
f.write("%s,%s\n"%(key,dic[key]))
This sort of outputs the format I'd like except for the following:
All of the dataframe columns are in Cell B1 and they're not complete... it's
A B C D E F G H I ... R S T U V W X Y
Z
and then the indexes and dataframe elements are all in columns A. i.e. Cells A2:A4 is
0 55 96 60 47 11 3 2 69 50 ... 3 23 26 3 15 53
78 95 49
1 72 48 12 25 32 57 11 84 5 ... 11 43 56 0 68 55
95 64 84
2 80 56 78 58 79 72 67 97 58 ... 84 34 18 21 71 20
72 36 37
I'd like the dataframes to be written to the csv in their entirety and obviously the values in discrete cells
You can try:
dic={'key1': create_random_df() , 'key2': create_random_df()}
with open('test.csv', 'w') as f:
for key in dic.keys():
df = dic[key]
df.insert(0,'Key', pd.Series([key]))
df.Key = df.Key.fillna('')
f.write(df.to_csv(index=False))
Related
I have one dataframe that I need to filter based on condition receiving in request from UI.
request example:
{
"table": "abc",
"condition": "A=98 and C=73 and D='rendom_char'"
}
dataframe sample:
A
B
C
D
0
85
39
54
td
1
39
51
23
abc
2
98
17
73
def
3
98
52
73
def
4
85
52
21
rst
5
61
89
31
xvz
so suppose if I get condition from UI that "condition": "A=98 and C=73 and D='def'" or "condition": "A=98 and C=73"
output should be like :
A
B
C
D
2
98
17
73
def
3
98
52
73
def
The issue I'm facing is how to convert that string condition getting from UI into python form so that I can apply filter on datafram.
Use DataFrame.query with replace = to double =:
d = {"condition": "A=98 and C=73 and D='def'"}
print (df.query(d['condition'].replace('=','==')))
A B C D
2 98 17 73 def
3 98 52 73 def
d = {"condition": "A=85 and C=21"}
print (df.query(d['condition'].replace('=','==')))
A B C D
4 85 52 21 rst
Alternatively, you can query pandas dataframe in SQL fashion with pandasql:
import pandasql as psql
...
req = {"table": "abc", "condition": "A=98 and C=73 and D='def'"}
q = f"select * from df where {req['condition']}"
print(psql.sqldf(q, locals()))
A B C D
0 98 17 73 def
1 98 52 73 def
I have a dataframe (df_input), and im trying to convert it to another dataframe (df_output), through applying a formula to each element in each row. The formula requires information about the the whole row (min, max, median).
df_input:
A B C D E F G H I J
2011-01-01 60 48 26 29 41 91 93 87 39 65
2011-01-02 88 52 24 99 1 27 12 26 64 87
2011-01-03 13 1 38 60 8 50 59 1 3 76
df_output:
F(A)F(B)F(C)F(D)F(E)F(F)F(G)F(H)F(I)F(J)
2011-01-01 93 54 45 52 8 94 65 37 2 53
2011-01-02 60 44 94 62 78 77 37 97 98 76
2011-01-03 53 58 16 63 60 9 31 44 79 35
Im trying to go from df_input to df_output, as above, after applying f(x) to each cell per row. The function foo is trying to map element x to f(x) by doing an OLS regression of the min, median and max of the row to some co-ordinates. This is done each period.
I'm aware that I iterate over the rows and then for each row apply the function to each element. Where i am struggling is getting the output of foo, into df_output.
for index, row in df_input.iterrows():
min=row.min()
max=row.max()
mean=row.mean()
#apply function to row
new_row = row.apply(lambda x: foo(x,min,max,mean)
#add this to df_output
help!
My current thinking is to build up the new df row by row? I'm trying to do that but im getting a lot of multiindex columns etc. Any pointers would be great.
thanks so much... merry xmas to you all.
Consider calculating row aggregates with DataFrame.* methods and then pass series values in a DataFrame.apply() across columns:
# ROW-WISE AGGREGATES
df['row_min'] = df.min(axis=1)
df['row_max'] = df.max(axis=1)
df['row_mean'] = df.mean(axis=1)
# COLUMN-WISE CALCULATION (DEFAULT axis=0)
new_df = df[list('ABCDEFGHIJ')].apply(lambda col: foo(col,
df['row_min'],
df['row_max'],
df['row_mean']))
Hi when I try to print a list, it prints out the directory and not the contents of win.txt. I'm trying to enumerate the txt into a list and split then append it to a, then do other things once get a to print. What am I doing wrong?
import os
win_path = os.path.join(home_dir, 'win.txt')
def roundedStr(num):
return str(int(round(num)))
a=[] # i declares outside the loop for recover later
for i,line in enumerate(win_path):
# files are iterable
if i==0:
t=line.split(' ')
else:
t=line.split(' ')
t[1:6]= map(int,t[1:6])
a.append(t) ## a have all the data
a.pop(0)
print a
prints out directory, like example c:\workspace\win.txt
NOT what I want
I want it to print the contents of win.txt
which takes t[1:6] as integers, like
11 21 31 41 59 21
and prints that out like that same way.
win.txt contains this
05/06/2017 11 21 31 41 59 21 3
05/03/2017 17 18 49 59 66 9 2
04/29/2017 22 23 24 45 62 5 2
04/26/2017 01 15 18 26 51 26 4
04/22/2017 21 39 41 48 63 6 3
04/19/2017 01 19 37 40 52 15 3
04/15/2017 05 22 26 45 61 13 3
04/12/2017 08 14 61 63 68 24 2
04/08/2017 23 36 51 53 60 15 2
04/05/2017 08 20 46 53 54 13 2
I just want [1]-[6]
I think what you want is to open the file 'win.txt', and read its content. Using the open function to create a file object, and a with block to scope it. See my example below. This will read the file, and take the first 6 numbers of each line.
import os
win_path = os.path.join(home_dir, 'win.txt')
a=[] # i declares outside the loop for recover later
with open(win_path, 'r') as file:
for i,line in enumerate(file):
line = line.strip()
print(line)
if i==0:
t=line.split(' ')
else:
t=line.split(' ')
t[1:7]= map(int,t[1:7])
t = t[1:7]
a.append(t) ## a have all the data
a.pop(0)
print (a)
I want to sort a pandas df but I'm having problems with the negative values.
import pandas as pd
df = pd.read_csv('File.txt', sep='\t', header=None)
#Suppress scientific notation (finally)
pd.set_option('display.float_format', lambda x: '%.8f' % x)
print(df)
print(df.dtypes)
print(df.shape)
b = df.sort(axis=0, ascending=True)
print(b)
This gives me the ascending order but completely disregards the sign.
SPATA1 -0.00000005
HMBOX1 0.00000005
SLC38A11 -0.00000005
RP11-571M6.17 0.00000004
GNRH1 -0.00000004
PCDHB8 -0.00000004
CXCL1 0.00000004
RP11-48B3.3 -0.00000004
RNFT2 -0.00000004
GRIK3 -0.00000004
ZNF483 0.00000004
RP11-627G18.1 0.00000003
Any ideas what I'm doing wrong?
Thanks
Loading your file with:
df = pd.read_csv('File.txt', sep='\t', header=None)
Since sort(....) is deprecated, you can use sort_values:
b = df.sort_values(by=[1], axis=0, ascending=True)
where [1] is your column of values. For me this returns:
0 1
0 ACTA1 -0.582570
1 MT-CO1 -0.543877
2 CKM -0.338265
3 MT-ND1 -0.306239
5 MT-CYB -0.128241
6 PDK4 -0.119309
8 GAPDH -0.090912
9 MYH1 -0.087777
12 RP5-940J5.9 -0.074280
13 MYH2 -0.072261
16 MT-ND2 -0.052551
18 MYL1 -0.049142
19 DES -0.048289
20 ALDOA -0.047661
22 ENO3 -0.046251
23 MT-CO2 -0.043684
26 RP11-799N11.1 -0.034972
28 TNNT3 -0.032226
29 MYBPC2 -0.030861
32 TNNI2 -0.026707
33 KLHL41 -0.026669
34 SOD2 -0.026166
35 GLUL -0.026122
42 TRIM63 -0.022971
47 FLNC -0.018180
48 ATP2A1 -0.017752
49 PYGM -0.016934
55 hsa-mir-6723 -0.015859
56 MT1A -0.015110
57 LDHA -0.014955
.. ... ...
60 RP1-178F15.4 0.013383
58 HSPB1 0.014894
54 UBB 0.015874
53 MIR1282 0.016318
52 ALDH2 0.016441
51 FTL 0.016543
50 RP11-317J10.2 0.016799
46 RP11-290D2.6 0.018803
45 RRAD 0.019449
44 MYF6 0.019954
43 STAC3 0.021931
41 RP11-138I1.4 0.023031
40 MYBPC1 0.024407
39 PDLIM3 0.025442
38 ANKRD1 0.025458
37 FTH1 0.025526
36 MT-RNR2 0.025887
31 HSPB6 0.027680
30 RP11-451G4.2 0.029969
27 AC002398.12 0.033219
25 MT-RNR1 0.040741
24 TNNC1 0.042251
21 TNNT1 0.047177
17 MT-ND3 0.051963
15 MTND1P23 0.059405
14 MB 0.063896
11 MYL2 0.076358
10 MT-ND5 0.076479
7 CA3 0.100221
4 MT-ND6 0.140729
[18152 rows x 2 columns]
For index.csv file, its fourth column has ten numbers ranging from 1-5. Each number can be regarded as an index, and each index corresponds with an array of numbers in filename.csv.
The row number of filename.csv represents the index, and each row has three numbers. My question is about using a nesting loop to transfer the numbers in filename.csv to index.csv.
from numpy import genfromtxt
import numpy as np
import csv
data1 = genfromtxt('filename.csv', delimiter=',')
data2 = genfromtxt('index.csv', delimiter=',')
f = open('index.csv','wb')
write = csv.writer(f, delimiter=',',quoting=csv.QUOTE_ALL)
for row in data2:
for ch_row in data1:
if ( data2[row,3] == ch_row ):
write.writerow(data1[data2[row,3],:])
For example, the fourth column of index.csv contains 1,2,5,3,4,1,4,5,2,3 and filename.csv contains:
# filename.csv
20 30 50
70 60 45
35 26 77
93 37 68
13 08 55
What I need is to write the indexed row from filename.csv to index.csv and store these number in 5th, 6th and 7th column:
# index.csv
# 4 5 6 7
... 1 20 30 50
... 2 70 60 45
... 5 13 08 55
... 3 35 26 77
... 4 93 37 68
... 1 20 30 50
... 4 93 37 68
... 5 13 08 55
... 2 70 60 45
... 3 35 26 77
Can anyone help me solve this problem?
You need to indent your last 2 lines. Also, it looks like you are writing to the file from which you are reading.