I am inputting multiple spreadsheets with multiple columns of data. For each spreadsheet, the maximum value of each column is found. Then, for each element in the column, the element is divided by the maximum value of that column. The output should be a value (between 0 and 1) for each element in the column in ascending order. This is appended to a list which should be added to the source spreadsheet as a column.
Currently, the nested loops are performing correctly apart from the final step, as far as I understand. Each column is added to the spreadsheet EXCEPT the values are for the final column of the source spreadsheet rather than values related to each individual column.
I have tried changing the indents to associate levels of the code with different parts (as I think this is the problem) and tried moving the appended column along in the dataframe, to no avail.
for i in distlist:
#listname = i[4:] + '_norm'
df2 = pd.read_excel(i,header=0,index_col=None, skip_blank_lines=True)
df3 = df2.dropna(axis=0, how='any')
cols = []
for column in df3:
cols.append(column)
for x in cols:
listname = x + ' norm'
maxval = df3[x].max()
print(maxval)
mylist = []
for j in df3[x]:
findNL = (j/maxval)
mylist.append(findNL)
df3[listname] = mylist
saveloc = 'E:/test/'
filename = i[:-18] + '_Normalised.xlsx'
df3.to_excel(saveloc+filename, index=False)
New columns are added to the output dataframe with bespoke headings relating to the field headers in the source spreadsheet and renamed according to (listname). The data in each one of these new columns is identical and relates to the final column in the spreadsheet. To me, it seems to be overwriting the values each time (as if looping through the entire spreadsheet, not outputting for each column), and adding it to the spreadsheet.
Any help would be much appreciated. I think it's something simple, but I haven't managed to work out what...
If I understand you correctly, you are overcomplicating things. You dont need a for loop for this. You can simplify your code:
# Make example dataframe, this is not provided
df = pd.DataFrame({'col1':[1, 2, 3, 4],
'col2':[5, 6, 7, 8]})
print(df)
col1 col2
0 1 5
1 2 6
2 3 7
3 4 8
Now we can use DataFrame.apply and use add_suffix to give the new columns _norm suffix and after that concat the columns to one final dataframe
df_conc = pd.concat([df, df.apply(lambda x: x/x.max()).add_suffix('_norm')],axis=1)
print(df_conc)
col1 col2 col1_norm col2_norm
0 1 5 0.25 0.625
1 2 6 0.50 0.750
2 3 7 0.75 0.875
3 4 8 1.00 1.000
Many thanks. I think I was just overcomplicating it. Incidentally, I think my code may do the same job, but because there is so little difference in the values, it wasn't notable.
Thanks for your help #Erfan
Related
I have a simple python 3.8 DataFrame with 8 columns (simply labeled 0, 1, 2, etc.) with approx. 3500 rows. I want a subset of this DataFrame where there are at least 2 columns in each row that are above 1. I would prefer not to have to check each column individually, but be able to check all columns. I know I can use the .any(1) to check all the columns, but I need there to be at least 2 columns that meet the threshold, not just one. Any help would be appreciated. Sample code below:
import pandas as pd
df = pd.DataFrame({0:[1,1,1,1,100],
1:[1,3,1,1,1],
2:[1,3,1,1,4],
3:[1,1,1,1,1],
4:[3,4,1,1,5],
5:[1,1,1,1,1]})
Easiest way I can think to sort/filter later would be to create another column at the end df[9] that houses the count:
df[9] = df.apply(lambda x: x.count() if x > 2, axis=1)
This code doesn't work, but I feel like it's close?
df[(df>1).sum(axis=1)>=2]
Explanation:
(df>1).sum(axis=1) gives the number of columns in that row that is greater than 1.
then with >=2 we filter those rows with at least 2 columns that meet the condition --which we counted as explained in the previous bullet
The value of x in the lambda is a Series, which can be indexed like this.
df[9] = df.apply(lambda x: x[x > 2].count(), axis=1)
I have a function which sends automated messages to clients, and takes as input all the columns from a dataframe like the one below.
name
phone
status
date
name_1
phone_1
sending
today
name_2
phone_2
sending
yesterday
I iterate through the dataframe with a pandas apply (axis=1) and use the values on the columns of each row as inputs to my function. At the end of it, after sending, it changes the status to "sent". The thing is I only want to send to the clients whose date reference is "today". Now, with pandas.apply(axis=1) this is perfectly doable, but in order to slice the clients with "today" value, I need to:
create a new dataframe with today's value,
remove it from the original, and then
reappend it to the original.
I thought about running through the whole dataframe and ignore the rows which have dates different than "today", but if my dataframe keeps growing, I'm afraid of the whole process becoming slower.
I saw examples of this being done with mask, although usually people only use 1 column, and I need more than just the one. Is there any way to do this with pandas apply?
Thank you.
I think you can use .loc to filter the data and apply func to it.
In [13]: df = pd.DataFrame(np.random.rand(5,5))
In [14]: df
Out[14]:
0 1 2 3 4
0 0.085870 0.013683 0.221890 0.533393 0.622122
1 0.191646 0.331533 0.259235 0.847078 0.649680
2 0.334781 0.521263 0.402030 0.973504 0.903314
3 0.189793 0.251130 0.983956 0.536816 0.703726
4 0.902107 0.226398 0.596697 0.489761 0.535270
if we want double the values of rows where the value in first column > 0.3
Out[16]:
0 1 2 3 4
2 0.334781 0.521263 0.402030 0.973504 0.903314
4 0.902107 0.226398 0.596697 0.489761 0.535270
In [18]: df.loc[df[0] > 0.3] = df.loc[df[0] > 0.3].apply(lambda x: x*2, axis=1)
In [19]: df
Out[19]:
0 1 2 3 4
0 0.085870 0.013683 0.221890 0.533393 0.622122
1 0.191646 0.331533 0.259235 0.847078 0.649680
2 0.669563 1.042527 0.804061 1.947008 1.806628
3 0.189793 0.251130 0.983956 0.536816 0.703726
4 1.804213 0.452797 1.193394 0.979522 1.070540
I have multiple Excel spreadsheets containing the same types of data but they are not in the same order. For example, if file 1 has the results of measurements A, B, C and D from River X printed in columns 1, 2, 3 and 4, respectively but file 2 has the same measurements taken for a different river, River Y, printed in columns 6, 7, 8, and 9 respectively, is there a way to use pandas to reorganise one dataframe to match the layout of another dataframe (i.e. make it so that Sheet2 has the measurements for River Y printed in columns 1, 2, 3 and 4)? Sometimes the data is presented horizontally, not vertically as described above, too. If I have the same measurements for, say, 400 different rivers on 400 separate sheets, but the presentation/layout of data is erratic with regards to each individual file, it would be useful to be able to put a single order on every spreadsheet without having to manually shift columns on Excel.
Is there a way to use pandas to reorganise one dataframe to match the layout of another dataframe?
You can get a list of columns from one of your dataframes and then sort that. Next you can use the sorted order to reorder your remaining dataframes. I've created an example below:
import pandas as pd
import numpy as np
# Create an example of your problem
root = 'River'
suffix = list('123')
cols_1 = [root + '_' + each_suffix for each_suffix in suffix]
cols_2 = [root + '_' + each_suffix for each_suffix in suffix[::]]
data = np.arange(9).reshape(3,3)
df_1 = pd.DataFrame(columns=cols_1, data=data)
df_2 = pd.DataFrame(columns=cols_2, data=data)
df_1
[out] River_1 River_2 River_3
0 0 1 2
1 3 4 5
2 6 7 8
df_2
[out] River_3 River_2 River_1
0 0 1 2
1 3 4 5
2 6 7 8
col_list = df_1.columns.to_list() # Get a list of column names use .sort() to sort in place or
sorted_col_list = sorted(col_list, reverse=False) # Use reverse True to invert the order
def rearrange_df_cols(df, target_order):
df = df[target_order]
print(df)
return df
rearrange_df_cols(df_1, sorted_col_list)
[out] River_1 River_2 River_3
0 0 1 2
1 3 4 5
2 6 7 8
rearrange_df_cols(df_2, sorted_col_list)
[out] River_1 River_2 River_3
0 2 1 0
1 5 4 3
2 8 7 6
You can write a function based on what's above and apply it to all of your file/sheets provided that all columns names exist (NB the must be written identically).
Sometimes the data is presented horizontally, not vertically as described above, too.
This would be better as a separate question. In principle you should check the dimension of your data e.g. df.shape and based of the shape you can either use df.transpose() and then your function to reorder the columns names or directly use your function to reorder the column names.
I'm trying to modify multiple column values in pandas.Dataframes with different increments in each column so that the values in each column do not overlap with each other when graphed on a line graph.
Here's the end goal of what I want to do: link
Let's say I have this kind of Dataframe:
Col1 Col2 Col3
0 0.3 0.2
1 1.1 1.2
2 2.2 2.4
3 3 3.1
but with hundreds of columns and thousands of values.
When graphing this on a line-graph on excel or matplotlib, the values overlap with each other, so I would like to separate each column by adding the same values for each column like so:
Col1(+0) Col2(+10) Col3(+20)
0 10.3 20.2
1 11.1 21.2
2 12.2 22.4
3 13 23.1
By adding the same value to one column and increasing by an increment of 10 over each column, I am able to see each line without it overlapping in one graph.
I thought of using loops and iterations to automate this value-adding process, but I couldn't find any previous solutions on Stackoverflow that addresses how I could change the increment value (e.g. from adding 0 in Col1 in one loop, then adding 10 to Col2 in the next loop) between different columns, but not within the values in a column. To make things worse, I'm a beginner with no clue about programming or data manipulation.
Since the data is in a CSV format, I first used Pandas to read it and store in a Dataframe, and selected the columns that I wanted to edit:
import pandas as pd
#import CSV file
df = pd.read_csv ('data.csv')
#store csv data into dataframe
df1 = pd.DataFrame (data = df)
# Locate columns that I want to edit with df.loc
columns = df1.loc[:, ' C000':]
here is where I'm stuck:
# use iteration with increments to add numbers
n = 0
for values in columns:
values = n + 0
print (values)
But this for-loop only adds one increment value (in this case 0), and adds it to all columns, not just the first column. Not only that, but I don't know how to add the next increment value for the next column.
Any possible solutions would be greatly appreciated.
IIUC ,just use df.add() over axis=1 with a list made from the length of df.columns:
df1 = df.add(list(range(0,len(df.columns)*10))[::10],axis=1)
Or as #jezrael suggested, better:
df1=df.add(range(0,len(df.columns)*10, 10),axis=1)
print(df1)
Col1 Col2 Col3
0 0 10.3 20.2
1 1 11.1 21.2
2 2 12.2 22.4
3 3 13.0 23.1
Details :
list(range(0,len(df.columns)*10))[::10]
#[0, 10, 20]
I would recommend you to avoid looping over the data frame as it is inefficient but rather think of adding to matrixes.
e.g.
import numpy as np
import pandas as pd
# Create your example df
df = pd.DataFrame(data=np.random.randn(10,3))
# Create a Matrix of ones
x = np.ones(df.shape)
# Multiply each column with an incremented value * 10
x = x * 10*np.arange(1,df.shape[1]+1)
# Add the matrix to the data
df + x
Edit: In case you do not want to increment with 10, 20 ,30 but 0,10,20 use this instead
import numpy as np
import pandas as pd
# Create your example df
df = pd.DataFrame(data=np.random.randn(10,3))
# Create a Matrix of ones
x = np.ones(df.shape)
# THIS LINE CHANGED
# Obmit the 1 so there is only an end value -> default start is 0
# Adjust the length of the vector
x = x * 10*np.arange(df.shape[1])
# Add the matrix to the data
df + x
0 546/001441
1 540/001495
2 544/000796
3 544/000797
4 544/000798
I have a column in my dataframe that I've provided above. It can have any number of rows depending on the data being crunched. It is one of many columns but the first three numbers match another columns data. I need to cut off everything after the first 3 numbers in order to append it to another dataframe based off of the similar values. Any ideas as to how to get only the first 3 numbers and cut off the remaining 8 values?
So far I've got the whole column singled out as a Series and also as a numpy.array in order to try to convert it to a str instead of an object.
I know this is getting me closer to an answer but i can't seem to figure out how to cut out the unnecessary values
testcut=dfwhynot[0][:3]
this cuts the string where i need it, but how do i do this for the whole column is what i can't figure out.
Assuming the name of your column is col, you can
# Split the column into two
df['col'] = df['col'].apply(lambda row: row.split('/'))
df[['col1', 'col2']] = pd.DataFrame(df_out.values.tolist())
col1 col2
0 546 001441
1 540 001495
2 544 000796
3 544 000797
4 544 000798
then get the minimal element of each col1 group
df.groupby('col1').min().reset_index()
resulting in
col1 col2
0 540 001495
1 544 000796
2 546 001441