I have a dataframe df of size 24x13 which appears as (I have displayed truncated version of 24x13 array which represents 12 months and 24 hours):
HE 1 2 3 4
0 1 1.8 2.5 3.5 8.5
1 2 2.6 2.9 4.3 8.7
2 3 4.4 2.3 5.3 4.3
3 4 2.6 2.1 4.2 5.3
How do I change this to look up table for each combination of hour and month and display the value in third column as follows:
Hour Month Value
1 1 1.8
1 2 2.5
1 3 3.5
I am trying the following and variation of it but this is not working:
pd.melt(df, id_vars=range(1,24), value_vars=range(1,12))
Edit 1:
df.columns
Index([u'HE', 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='object')
df.shape
(24, 13)
df.set_index('HE').stack().reset_index()
Output:
HE level_1 0
0 1 1 1.8
1 1 2 2.5
2 1 3 3.5
3 1 4 8.5
4 2 1 2.6
OR using melt
df.melt(id_vars='HE').sort_values(by=['HE','variable']
Output:
HE variable value
0 1 1 1.8
4 1 2 2.5
8 1 3 3.5
12 1 4 8.5
1 2 1 2.6
Related
I have below dataset.
Math Literature Biology date student
4 2 5 2019-08-25 A
4 5 4 2019-08-08 A
5 4 5 2019-08-23 A
5 5 5 2019-08-15 A
5 5 5 2019-07-19 A
5 5 5 2019-07-15 A
5 5 5 2019-07-03 A
5 5 5 2019-06-26 A
1 1 2 2019-06-18 A
2 3 3 2019-06-14 A
5 5 5 2019-05-01 A
2 1 3 2019-04-26 A
I need to develop a solution in powerbi so in output I have cumulative average per subject per month
For example
April May June July August
Math | 2 3.5 3 3.75 4
Literature | 1 3 3 3.75 3.83
Biology | 3 4 3.6 4.125 4.33
Can you help?
You can use a matrix visualization for this.
Create a month-year variable and use it in the columns.
Use Average of Math,Literature and Biology in values
Under the format pane --> Values --> Show on rows --> Select this
This should give the view you are looking for. You can edit the value headers to your requirement.
I am trying to write a function that calculates the average of identical columns of different dataframes stored in a list:
def mean(dfs):
# declare an empty dataframe
df_mean = pd.DataFrame()
# assign the first column from each raw data framework to df
for i in range(len(dfs)):
dfs[i].set_index(['Time'], inplace=True)
for j in dfs[0].columns:
for i in range(len(dfs)):
df_mean[j] = pd.concat([df_mean,dfs[i][j]], axis=1).mean(axis=1)
return df_mean
dfs = []
l1 = [[1,6,2,6,7],[2,3,2,6,8],[3,3,2,8,8],[4,5,2,6,8],[5,3,9,6,8]]
l2 = [[1,7,2,5,7],[2,3,0,6,8],[3,3,3,6,8],[4,3,7,6,8],[5,3,0,6,8]]
dfs.append(pd.DataFrame(l1, columns=['Time','25','50','75','100']))
dfs.append(pd.DataFrame(l2, columns=['Time','25','50','75','100']))
mean(dfs)
However, I got out only the mean of the first column right!
Option 1
Use python's sum, which well default to reducing the list based on the individual object's __add__ method. Then just divide by the length of the list.
sum(dfs) / len(dfs)
Time 25 50 75 100
0 1.0 6.5 2.0 5.5 7.0
1 2.0 3.0 1.0 6.0 8.0
2 3.0 3.0 2.5 7.0 8.0
3 4.0 4.0 4.5 6.0 8.0
4 5.0 3.0 4.5 6.0 8.0
Option 2
Reconstruct while using numpy's mean function
pd.DataFrame(
np.mean([d.values for d in dfs], 0),
dfs[0].index, dfs[0].columns)
Time 25 50 75 100
0 1.0 6.5 2.0 5.5 7.0
1 2.0 3.0 1.0 6.0 8.0
2 3.0 3.0 2.5 7.0 8.0
3 4.0 4.0 4.5 6.0 8.0
4 5.0 3.0 4.5 6.0 8.0
Use concat on Time indexed list of dataframes, and groupby larger dataframe on Time and take mean
In [275]: pd.concat([d.set_index('Time') for d in dfs]).groupby(level='Time').mean()
Out[275]:
25 50 75 100
Time
1 6.5 2.0 5.5 7.0
2 3.0 1.0 6.0 8.0
3 3.0 2.5 7.0 8.0
4 4.0 4.5 6.0 8.0
5 3.0 4.5 6.0 8.0
Or, since Time column is anyway common for both, atleast in this usecase
In [289]: pd.concat(dfs).groupby(level=0).mean()
Out[289]:
Time 25 50 75 100
0 1.0 6.5 2.0 5.5 7.0
1 2.0 3.0 1.0 6.0 8.0
2 3.0 3.0 2.5 7.0 8.0
3 4.0 4.0 4.5 6.0 8.0
4 5.0 3.0 4.5 6.0 8.0
Details
In [276]: dfs
Out[276]:
[ Time 25 50 75 100
0 1 6 2 6 7
1 2 3 2 6 8
2 3 3 2 8 8
3 4 5 2 6 8
4 5 3 9 6 8, Time 25 50 75 100
0 1 7 2 5 7
1 2 3 0 6 8
2 3 3 3 6 8
3 4 3 7 6 8
4 5 3 0 6 8]
In [277]: pd.concat([d.set_index('Time') for d in dfs])
Out[277]:
25 50 75 100
Time
1 6 2 6 7
2 3 2 6 8
3 3 2 8 8
4 5 2 6 8
5 3 9 6 8
1 7 2 5 7
2 3 0 6 8
3 3 3 6 8
4 3 7 6 8
5 3 0 6 8
I have a dataframe of size 12x24 which appears as (I have displayed truncated version):
HE 1 2 3 4 5
0 1 1.8 2.5 3.5 8.5
1 2 2.6 2.9 4.3 8.7
2 3 4.4 2.3 5.3 4.3
3 4 2.6 2.1 4.2 5.3
The column names are number 1 through 12 (representing months) and rows are numbered 1 through 24 (representing hours).
I have another DateTable dataframe which has data as follows:
Date Month Hour
2001-01-01 1 1
2001-02-01 2 4
2001-01-05 1 3
2011-01-31 3 2
2012-01-01 1 5
I want to broadcast the values from 12x24 array into the DateTable to get the following:
Date Month Hour Values
2001-01-01 1 1 3.5
2001-02-01 2 4 5.3
2001-01-05 1 3 2.5
2011-01-31 3 2 2.6
2012-01-01 1 5 1.8
I envision creating some kind of multiindex from the 12x24 table and using against the DateTable but not quite sure about the syntax as I am struggling with syntax.
I have a data set of daily temperatures for which I want to calculate 20 year means. The data look like this:
1974 1 1 5.3 4.6 7.3 3.4
1974 1 2 3.3 7.2 4.5 6.5
...
2005 12 364 4.2 5.2 3.3 4.6
2005 12 365 3.1 5.5 2.6 6.8
There is no header in the file but the first column contains the year, the second column the month, and the third column the day of the year. The rest of the columns are temperature data.
I want to calculate the average temperature for each day over a period of 20 years. I thought the best way to do that would be to group the data by day and calculate the mean of each day for a specific range of years. Here is my code:
import pandas as pd
hist_fn = 'tmean_daily_1974_2005.txt'
twenty_year_fn = '20_yr_mean_1974_1993.txt'
start = 1974
end = 1993
hist_mean = pd.read_csv(hist_fn, sep='\s+', header=None)
# Limit dataframe to only the 20 years for which I want the mean calculated
interval_mean = hist_mean[(hist_mean[0]>=start) & (hist_mean[0]<=end)]
# Rename the first column to reflect what mean this file is displaying
interval_mean.iloc[:, 0] = ("%s-%s" % (start, end))
# Generate mean for each day spread across all the years in the dataframe
interval_mean.iloc[:, 3:] = interval_mean.groupby(2, as_index=False).mean().iloc[:, 2:]
# Write multiyear mean to txt
interval_mean.to_csv(twenty_year_fn, sep='\t', header=False, index=False)
The data set spans longer than 20 years and the method I used has worked for the first 20 year interval but gives me a (mostly) empty text file for any other set of years entered.
So when I use these inputs it works:
start = 1974
end = 1993
and it produces a file that looks like this:
1974-1993 1 1 4.33 5.25 6.84 3.67
1974-1993 1 2 7.23 6.22 5.65 6.23
...
1974-1993 12 364 5.12 4.34 5.21 2.16
1974-1993 12 365 4.81 5.95 3.56 6.78
but when I change the inputs to this:
start = 1975
end = 1994
it produces a .txt file with no temperatures:
1975-1994 1 1
1975-1994 1 2
...
1975-1994 12 364
1975-1994 12 365
I don't understand why this method works for the first 20 year interval but none of the subsequent intervals. Is it something to do with the way the data is organized or how it is being sliced?
Now when that's out of the way, we can talk about the problem you presented:
The strange behavior is due to the fact that pandas matches indices on assignment, and slicing preserves the original indices. That means that when setting
interval_mean.iloc[:, 3:] = interval_mean.groupby(2, as_index=False).mean().iloc[:, 2:]
Note that interval_mean.groupby(2, as_index=False).mean() has indices 0, ... , 30 (since as_index=False makes the groupby operation create new indices. Otherwise, it would have been the day number).On the other had, interval_mean has the original indices from hist_mean, meaning the first time (first 20 years) it has the indices 0, ..., ~20*365 and the second time is has indices starting from arround 20*365 and counting up.
This is a bit confusing at first, but pandas offer great documentation about it, and people quickly discover why it is so useful.
I'll to explain what happens with an example:
Assume we have the following DataFrame:
df = pd.DataFrame(np.reshape(np.random.randint(5, size=30), [-1,3]))
df
0 1 2
0 1 1 2
1 2 1 1
2 0 1 2
3 0 2 0
4 2 1 0
5 0 1 2
6 2 2 1
7 1 0 2
8 0 1 0
9 1 2 0
Note that the column names are 0,1,2 and the row names (the index) are 0, ..., 9.
When we preform groupby we obtain
df.groupby(0, as_index=False).mean()
0 1 2
0 0 1.250000 1.000000
1 1 1.000000 1.333333
2 2 1.333333 0.666667
(The index equals to the columns grouped by just because draw numbers between 0 to 2). Now, when will do assignments to df.loc, it will replace every cell by the corresponding cell in the assignee, if such cell exists. Otherwise, it will leave NA.
df.loc[:,:] = df.groupby(0, as_index=False).mean()
df
0 1 2
0 0.0 1.250000 1.000000
1 1.0 1.000000 1.333333
2 2.0 1.333333 0.666667
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 NaN NaN NaN
7 NaN NaN NaN
8 NaN NaN NaN
9 NaN NaN NaN
And when you write NA to csv, it leaves the cell blank.
The last piece of the puzzle is how interval_mean preserved the original indices, but this is because slicing preserves the original indices:
df[df[1] > 1]
0 1 2
3 0 2 0
6 2 2 1
9 1 2 0
This question already has answers here:
Pandas Merging 101
(8 answers)
Closed 4 years ago.
I have a folder with numerous csv files which look like this:
csv1
2006 Percent Land_Use
0 13 5.379564 Developed
1 8 25.781580 Grass/Pasture
2 4 54.265050 Crop
3 15 0.363983 Water
4 16 6.244104 Wetlands
5 6 4.691764 Forest
6 1 3.031494 Alfalfa
7 11 0.137424 Shrubland
8 5 0.003671 Vetch
9 3 0.055412 Barren
10 7 0.009531 Grass
11 12 0.036423 Tree
csv2
2007 Percent Land_Use
0 13 2.742430 Developed
1 4 56.007242 Crop
2 8 24.227963 Grass/Pasture
3 16 8.839979 Wetlands
4 6 6.181062 Forest
5 1 1.446668 Alfalfa
6 15 0.366116 Water
7 3 0.127760 Barren
8 11 0.034426 Shrubland
9 7 0.000827 Grass
10 12 0.025528 Tree
csv3
2008 Percent Land_Use
0 13 1.863809 Developed
1 8 31.455578 Grass/Pasture
2 4 57.896856 Crop
3 16 2.693929 Wetlands
4 6 4.417966 Forest
5 1 1.239176 Alfalfa
6 7 0.130849 Grass
7 15 0.266536 Water
8 11 0.004571 Shrubland
9 3 0.030731 Barren
and I want to merge them all together into one DataFrame on Land_Use
I am reading in the files like this:
pth = (r'G:\')
for f in os.listdir(pth):
df=pd.read_csv(os.path.join(pth,f)
but I can't figure out how to merge all the individual dataframes after that. I figured out how to concat them but that isn't what I want. The type of merge I want is outer.
If I were to use a pathway to each csv file I would merge them like this, but I do NOT want to set a pathway to each file as there are many of them:
one=pd.read_csv(r'G:\one.csv')
two=pd.read_csv(r'G:\two.csv')
three=pd.read_csv(r'G:\three.csv')
merge=pd.merge(one,two, on=['Land_Use'], how='outer')
mergetwo=pd.merge(merge,three,on=['Land_Use'], how='outer')
I think you can use in python 3:
import functools
dfs = [df1,df2,df3]
df = functools.reduce(lambda left,right: pd.merge(left,right,on='Land_Use',how='outer'),dfs)
print (df)
2006 Percent_x Land_Use 2007 Percent_y 2008 Percent
0 13 5.379564 Developed 13.0 2.742430 13.0 1.863809
1 8 25.781580 Grass/Pasture 8.0 24.227963 8.0 31.455578
2 4 54.265050 Crop 4.0 56.007242 4.0 57.896856
3 15 0.363983 Water 15.0 0.366116 15.0 0.266536
4 16 6.244104 Wetlands 16.0 8.839979 16.0 2.693929
5 6 4.691764 Forest 6.0 6.181062 6.0 4.417966
6 1 3.031494 Alfalfa 1.0 1.446668 1.0 1.239176
7 11 0.137424 Shrubland 11.0 0.034426 11.0 0.004571
8 5 0.003671 Vetch NaN NaN NaN NaN
9 3 0.055412 Barren 3.0 0.127760 3.0 0.030731
10 7 0.009531 Grass 7.0 0.000827 7.0 0.130849
11 12 0.036423 Tree 12.0 0.025528 NaN NaN
In python 2:
df = reduce(lambda left,right: pd.merge(left,right,on='Land_Use',how='outer'),dfs)
Working solution with glob:
import pandas as pd
import functools
import glob
pth = 'a/*.csv'
files = glob.glob(pth)
dfs = [pd.read_csv(f, sep=';') for f in files]
df = functools.reduce(lambda left,right: pd.merge(left,right,on='Land_Use', how='outer'),dfs)
print (df)
2006 Percent_x Land_Use 2008 Percent_y 2007 Percent
0 13 5.379564 Developed 13.0 1.863809 13.0 2.742430
1 8 25.781580 Grass/Pasture 8.0 31.455578 8.0 24.227963
2 4 54.265050 Crop 4.0 57.896856 4.0 56.007242
3 15 0.363983 Water 15.0 0.266536 15.0 0.366116
4 16 6.244104 Wetlands 16.0 2.693929 16.0 8.839979
5 6 4.691764 Forest 6.0 4.417966 6.0 6.181062
6 1 3.031494 Alfalfa 1.0 1.239176 1.0 1.446668
7 11 0.137424 Shrubland 11.0 0.004571 11.0 0.034426
8 5 0.003671 Vetch NaN NaN NaN NaN
9 3 0.055412 Barren 3.0 0.030731 3.0 0.127760
10 7 0.009531 Grass 7.0 0.130849 7.0 0.000827
11 12 0.036423 Tree NaN NaN 12.0 0.025528
I am not allowed to comment, so I am unsure of what you exactly want.
You can try using one.merge(two, on=['Land_Use'], how='outer').merge(three,on=['Land_Use'], how='outer'). Let me know if you wanted something else.
If you have many dataframes, you can try using the reduce function. First create a list containing all the dataframes dataframes = [one, two, three, four, ... , twenty] You can add them into the list by using list comprehensions or by appending them into the list in your loop.
Then if you want to combine them based on Land_Use, you can use df_final = reduce(lambda left,right: pd.merge(left,right,on=['Land_Use'], how='outer'), dataframes)
Note: The reduce function is in the functools package in python 3+