Is there an easy way to convert from Type A to Type B.
Note : Kutools (Plugin in Excel) provides a solution for it but that is not robust and does not seem scalable.
Any workaround for this ?
Considering you can make the df look like below : (just remove the top row which says Type A)
GDP per capita 1950 1951 1952 1953
0 Antigua and Barbuda 3544 3633 3723 3817
1 Argentina 7540 7612 7019 7198
2 Armenia 1862 1834 1914 1958
3 Aruba 3897 3994 4094 4196
4 Australia 12073 12229 12084 12228
5 Austria 6919 7382 7386 7692
Using pd.melt()
>>pd.melt(df,id_vars='GDP per capita',var_name='Year',value_name='GDP Value')
GDP per capita Year GDP Value
0 Antigua and Barbuda 1950 3544
1 Argentina 1950 7540
2 Armenia 1950 1862
3 Aruba 1950 3897
4 Australia 1950 12073
5 Austria 1950 6919
6 Antigua and Barbuda 1951 3633
7 Argentina 1951 7612
8 Armenia 1951 1834
9 Aruba 1951 3994
10 Australia 1951 12229
11 Austria 1951 7382
12 Antigua and Barbuda 1952 3723
13 Argentina 1952 7019
14 Armenia 1952 1914
15 Aruba 1952 4094
16 Australia 1952 12084
17 Austria 1952 7386
18 Antigua and Barbuda 1953 3817
19 Argentina 1953 7198
20 Armenia 1953 1958
21 Aruba 1953 4196
22 Australia 1953 12228
23 Austria 1953 7692
To get the exact look like the image you have posted use:
df1=pd.melt(df,id_vars='GDP per capita',var_name='Year',value_name='GDP Value')
df1.rename(columns={'GDP per capita':'Country'},inplace=True)
df1['GDP'] = 'GDP per capita'
df1 = df1[['GDP','Country','Year','GDP Value']]
df1.to_csv('filepath+filename.csv,index=False)
Related
This question already has answers here:
Pandas Melt Function
(2 answers)
Closed 1 year ago.
I'm trying to transpose a few columns while keeping the other columns. I'm having a hard time with pivot codes or transpose codes as it doesn't really give me the output I need.
Can anyone help?
I have this data frame:
EmpID
Goal
week 1
week 2
week 3
week 4
1
556
54
33
24
54
2
342
32
32
56
43
3
534
43
65
64
21
4
244
45
87
5
22
My expected dataframe output is:
EmpID
Goal
Weeks
Actual
1
556
week 1
54
1
556
week 2
33
1
556
week 3
24
1
556
week 4
54
and so on until the full employee IDs are listed..
Something like this.
# Python - melt DF
import pandas as pd
d = {'Country Code': [1960, 1961, 1962, 1963, 1964, 1965, 1966],
'ABW': [2.615300, 2.734390, 2.678430, 2.929920, 2.963250, 3.060540, 4.349760],
'AFG': [0.249760, 0.218480, 0.210840, 0.217240, 0.211410, 0.209910, 0.671330],
'ALB': ['NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 1.12214]}
df = pd.DataFrame(data=d)
print(df)
df1 = (df.melt(['Country Code'], var_name='Year', value_name='Econometric_Metric')
.sort_values(['Country Code','Year'])
.reset_index(drop=True))
print(df1)
df2 = (df.set_index(['Country Code'])
.stack(dropna=False)
.reset_index(name='Econometric_Metric')
.rename(columns={'level_1':'Year'}))
print(df2)
# BEFORE
ABW AFG ALB Country Code
0 2.61530 0.24976 NaN 1960
1 2.73439 0.21848 NaN 1961
2 2.67843 0.21084 NaN 1962
3 2.92992 0.21724 NaN 1963
4 2.96325 0.21141 NaN 1964
5 3.06054 0.20991 NaN 1965
6 4.34976 0.67133 1.12214 1966
# AFTER
Country Code Year Econometric_Metric
0 1960 ABW 2.6153
1 1960 AFG 0.24976
2 1960 ALB NaN
3 1961 ABW 2.73439
4 1961 AFG 0.21848
5 1961 ALB NaN
6 1962 ABW 2.67843
7 1962 AFG 0.21084
8 1962 ALB NaN
9 1963 ABW 2.92992
10 1963 AFG 0.21724
11 1963 ALB NaN
12 1964 ABW 2.96325
13 1964 AFG 0.21141
14 1964 ALB NaN
15 1965 ABW 3.06054
16 1965 AFG 0.20991
17 1965 ALB NaN
18 1966 ABW 4.34976
19 1966 AFG 0.67133
20 1966 ALB 1.12214
Country Code Year Econometric_Metric
0 1960 ABW 2.6153
1 1960 AFG 0.24976
2 1960 ALB NaN
3 1961 ABW 2.73439
4 1961 AFG 0.21848
5 1961 ALB NaN
6 1962 ABW 2.67843
7 1962 AFG 0.21084
8 1962 ALB NaN
9 1963 ABW 2.92992
10 1963 AFG 0.21724
11 1963 ALB NaN
12 1964 ABW 2.96325
13 1964 AFG 0.21141
14 1964 ALB NaN
15 1965 ABW 3.06054
16 1965 AFG 0.20991
17 1965 ALB NaN
18 1966 ABW 4.34976
19 1966 AFG 0.67133
20 1966 ALB 1.12214
Also, take a look at the link below, for more info.
https://www.dataindependent.com/pandas/pandas-melt/
I have the following pandas datframe
For each country I wish to create as many rows as the number of years it exists.
For instance, the US will have 201 rows, Canada 95 and so forth.
I thought of doing something like:
for row in df.iterrows():
for range(row['styear'], row['endyear']):
df.append(row)
Any ideas how to make this work?
You can create a new column with the range of years, and then explode that column
# sample dataframe
df = pd.DataFrame({
'country': ['United States', 'Canada', 'Bahamas', 'Cuba'],
'styear': [1816, 1920, 1973, 1902],
'endyear': [2016, 2016, 2016, 1906]
})
df['allyears'] = [range(start, end+1)
for start, end in zip(df.styear, df.endyear)]
df = df.explode('allyears')
print(df)
Output
country styear endyear allyears
0 United States 1816 2016 1816
0 United States 1816 2016 1817
0 United States 1816 2016 1818
0 United States 1816 2016 1819
0 United States 1816 2016 1820
.. ... ... ... ...
3 Cuba 1902 1906 1902
3 Cuba 1902 1906 1903
3 Cuba 1902 1906 1904
3 Cuba 1902 1906 1905
3 Cuba 1902 1906 1906
[347 rows x 4 columns]
My panda data frame looks like as follows:
Country Code 1960 1961 1962 1963 1964 1965 1966 1967 1968 ... 2015
ABW 2.615300 2.734390 2.678430 2.929920 2.963250 3.060540 ... 4.349760
AFG 0.249760 0.218480 0.210840 0.217240 0.211410 0.209910 ... 0.671330
ALB NaN NaN NaN NaN NaN NaN NaN NaN NaN ... 1.12214
...
How can I transpose it that it looks like as follows?
Country_Code Year Econometric_Metric
ABW 1960 2.615300
ABW 1961 2.734390
ABW 1962 2.678430
...
ABW 2015 4.349760
AFG 1960 0.249760
AFG 1961 0.218480
AFG 1962 0.210840
...
AFG 2015 0.671330
ALB 1960 NaN
ALB 1961 NaN
ALB 1962 NaN
ALB 2015 1.12214
...
Thanks.
I think need melt with sort_values:
df = (df.melt(['Country Code'], var_name='Year', value_name='Econometric_Metric')
.sort_values(['Country Code','Year'])
.reset_index(drop=True))
Or set_index with stack:
df = (df.set_index(['Country Code'])
.stack(dropna=False)
.reset_index(name='Econometric_Metric')
.rename(columns={'level_1':'Year'}))
print (df.head(10))
Country Code Year Econometric_Metric
0 ABW 1960 2.61530
1 ABW 1961 2.73439
2 ABW 1962 2.67843
3 ABW 1963 2.92992
4 ABW 1964 2.96325
5 ABW 1965 3.06054
6 ABW 1966 NaN
7 ABW 1967 NaN
8 ABW 1968 NaN
9 ABW 2015 4.34976
I have data from the World Bank that look like this:
Country Name Country Code 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Aruba ABW 80326 83195 85447 87276 89004 90858 92894 94995 97015 98742 100031 100830 101218 101342 101416 101597 101936 102393 102921 103441 103889
It's population data from 250 some countries and I've just shown the first one for the sake of example. How would I be able to transpose this so that each country and year are on a single row like this?
Country Name Country Code Year Population
Aruba ABW 1995 80326
Aruba ABW 1996 83195
Aruba ABW 1997 85447
Aruba ABW 1998 87276
And so on and so forth
You could use pd.melt.
pd.melt(df, id_vars=['Country Name', 'Country Code'],
var_name='Year', value_name='Population')
Or alternatively, could add the Country Name and Country Code to the index, stack, then reset the index
df = df.set_index(['Country Name', 'Country Code']).stack().reset_index()
but then you'll have to set the column names post-process. pd.melt is probably nicer for this, and is most likely faster as well.
Demo
>>> pd.melt(df, id_vars=['Country Name', 'Country Code'],
var_name='Year', value_name='Population')
Country Name Country Code Year Population
0 Aruba ABW 1995 80326
1 Aruba ABW 1996 83195
2 Aruba ABW 1997 85447
3 Aruba ABW 1998 87276
4 Aruba ABW 1999 89004
5 Aruba ABW 2000 90858
6 Aruba ABW 2001 92894
7 Aruba ABW 2002 94995
8 Aruba ABW 2003 97015
9 Aruba ABW 2004 98742
10 Aruba ABW 2005 100031
11 Aruba ABW 2006 100830
12 Aruba ABW 2007 101218
13 Aruba ABW 2008 101342
14 Aruba ABW 2009 101416
15 Aruba ABW 2010 101597
16 Aruba ABW 2011 101936
17 Aruba ABW 2012 102393
18 Aruba ABW 2013 102921
19 Aruba ABW 2014 103441
20 Aruba ABW 2015 103889
import pandas as pd
olympics = pd.read_csv('olympics.csv')
Edition NOC Medal
0 1896 AUT Silver
1 1896 FRA Gold
2 1896 GER Gold
3 1900 HUN Bronze
4 1900 GBR Gold
5 1900 DEN Bronze
6 1900 USA Gold
7 1900 FRA Bronze
8 1900 FRA Silver
9 1900 USA Gold
10 1900 FRA Silver
11 1900 GBR Gold
12 1900 SUI Silver
13 1900 ZZX Gold
14 1904 HUN Gold
15 1904 USA Bronze
16 1904 USA Gold
17 1904 USA Silver
18 1904 CAN Gold
19 1904 USA Silver
I can pivot the data frame to have some aggregate value
pivot = olympics.pivot_table(index='Edition', columns='NOC', values='Medal', aggfunc='count')
NOC AUT CAN DEN FRA GBR GER HUN SUI USA ZZX
Edition
1896 1.0 NaN NaN 1.0 NaN 1.0 NaN NaN NaN NaN
1900 NaN NaN 1.0 3.0 2.0 NaN 1.0 1.0 2.0 1.0
1904 NaN 1.0 NaN NaN NaN NaN 1.0 NaN 4.0 NaN
Rather than having the total number of medals in values= , I am interested to have a tuple (a triple) with (#Gold, #Silver, #Bronze), (0,0,0) for NaN
How do I do that succinctly and elegantly?
No need to use pivot_table, as pivot is perfectly fine with tuple for a value
value_counts to count all medals
create multi-index for all combinations of countries, dates, medals
reindex with fill_values=0
counts = df.groupby(['Edition', 'NOC']).Medal.value_counts()
mux = pd.MultiIndex.from_product(
[c.values for c in counts.index.levels], names=counts.index.names)
counts = counts.reindex(mux, fill_value=0).unstack('Medal')
counts = counts[['Bronze', 'Silver', 'Gold']]
pd.Series([tuple(l) for l in counts.values.tolist()], counts.index).unstack()