Build new dataframe with for loop given condition in another dataframe
This is the dataframe I want to iterate through. The index is set for both countries
and year
.
ISO_code gini ECONOMIC FREEDOM rank quartile 1a_government_consumption
countries year
Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
1995 ARG 48.9 6.95 37.0 2.0 8.058824
2000 ARG 51.1 7.34 37.0 2.0 6.877627
2001 ARG 53.3 6.84 56.0 2.0 6.752473
2002 ARG 53.8 6.28 79.0 3.0 6.905961
2003 ARG 50.7 6.16 86.0 3.0 7.264992
Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
1985 BOL 48.9 6.95 37.0 2.0 8.058824
1995 BOL 51.1 7.34 37.0 2.0 6.877627
2000 BOL 53.3 6.84 56.0 2.0 6.752473
2001 BOL 53.8 6.28 79.0 3.0 6.905961
2002 BOL 50.7 6.16 86.0 3.0 7.264992
Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
1990 CHI 48.9 6.95 37.0 2.0 8.058824
1995 CHI 51.1 7.34 37.0 2.0 6.877627
1999 CHI 53.3 6.84 56.0 2.0 6.752473
2002 CHI 53.8 6.28 79.0 3.0 6.905961
2003 CHI 50.7 6.16 86.0 3.0 7.264992
I would like to create a for loop that returns a dataframe like this one:
countries change gini change ef
Argentina + +
Bolivia - +
Chile - -
countries
is simply the columns with the country names from the previous dataframe.change gini
should be the percentage difference between the last value of thegini
column for each country and the most recent one. If the percentage increase is positive, then it should show a+
; if it is negativa then it should show a-
.change ef
follows the same logic as thechange gini
in the new dataframe, with the only difference that the values used for calculating the percentage change come from theECONOMIC FREEDOM
column in the original dataframe.
python pandas loops dataframe for-loop
add a comment |
This is the dataframe I want to iterate through. The index is set for both countries
and year
.
ISO_code gini ECONOMIC FREEDOM rank quartile 1a_government_consumption
countries year
Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
1995 ARG 48.9 6.95 37.0 2.0 8.058824
2000 ARG 51.1 7.34 37.0 2.0 6.877627
2001 ARG 53.3 6.84 56.0 2.0 6.752473
2002 ARG 53.8 6.28 79.0 3.0 6.905961
2003 ARG 50.7 6.16 86.0 3.0 7.264992
Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
1985 BOL 48.9 6.95 37.0 2.0 8.058824
1995 BOL 51.1 7.34 37.0 2.0 6.877627
2000 BOL 53.3 6.84 56.0 2.0 6.752473
2001 BOL 53.8 6.28 79.0 3.0 6.905961
2002 BOL 50.7 6.16 86.0 3.0 7.264992
Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
1990 CHI 48.9 6.95 37.0 2.0 8.058824
1995 CHI 51.1 7.34 37.0 2.0 6.877627
1999 CHI 53.3 6.84 56.0 2.0 6.752473
2002 CHI 53.8 6.28 79.0 3.0 6.905961
2003 CHI 50.7 6.16 86.0 3.0 7.264992
I would like to create a for loop that returns a dataframe like this one:
countries change gini change ef
Argentina + +
Bolivia - +
Chile - -
countries
is simply the columns with the country names from the previous dataframe.change gini
should be the percentage difference between the last value of thegini
column for each country and the most recent one. If the percentage increase is positive, then it should show a+
; if it is negativa then it should show a-
.change ef
follows the same logic as thechange gini
in the new dataframe, with the only difference that the values used for calculating the percentage change come from theECONOMIC FREEDOM
column in the original dataframe.
python pandas loops dataframe for-loop
add a comment |
This is the dataframe I want to iterate through. The index is set for both countries
and year
.
ISO_code gini ECONOMIC FREEDOM rank quartile 1a_government_consumption
countries year
Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
1995 ARG 48.9 6.95 37.0 2.0 8.058824
2000 ARG 51.1 7.34 37.0 2.0 6.877627
2001 ARG 53.3 6.84 56.0 2.0 6.752473
2002 ARG 53.8 6.28 79.0 3.0 6.905961
2003 ARG 50.7 6.16 86.0 3.0 7.264992
Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
1985 BOL 48.9 6.95 37.0 2.0 8.058824
1995 BOL 51.1 7.34 37.0 2.0 6.877627
2000 BOL 53.3 6.84 56.0 2.0 6.752473
2001 BOL 53.8 6.28 79.0 3.0 6.905961
2002 BOL 50.7 6.16 86.0 3.0 7.264992
Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
1990 CHI 48.9 6.95 37.0 2.0 8.058824
1995 CHI 51.1 7.34 37.0 2.0 6.877627
1999 CHI 53.3 6.84 56.0 2.0 6.752473
2002 CHI 53.8 6.28 79.0 3.0 6.905961
2003 CHI 50.7 6.16 86.0 3.0 7.264992
I would like to create a for loop that returns a dataframe like this one:
countries change gini change ef
Argentina + +
Bolivia - +
Chile - -
countries
is simply the columns with the country names from the previous dataframe.change gini
should be the percentage difference between the last value of thegini
column for each country and the most recent one. If the percentage increase is positive, then it should show a+
; if it is negativa then it should show a-
.change ef
follows the same logic as thechange gini
in the new dataframe, with the only difference that the values used for calculating the percentage change come from theECONOMIC FREEDOM
column in the original dataframe.
python pandas loops dataframe for-loop
This is the dataframe I want to iterate through. The index is set for both countries
and year
.
ISO_code gini ECONOMIC FREEDOM rank quartile 1a_government_consumption
countries year
Argentina 1980 ARG 40.8 4.25 80.0 4.0 6.911765
1995 ARG 48.9 6.95 37.0 2.0 8.058824
2000 ARG 51.1 7.34 37.0 2.0 6.877627
2001 ARG 53.3 6.84 56.0 2.0 6.752473
2002 ARG 53.8 6.28 79.0 3.0 6.905961
2003 ARG 50.7 6.16 86.0 3.0 7.264992
Bolivia 1980 BOL 40.8 4.25 80.0 4.0 6.911765
1985 BOL 48.9 6.95 37.0 2.0 8.058824
1995 BOL 51.1 7.34 37.0 2.0 6.877627
2000 BOL 53.3 6.84 56.0 2.0 6.752473
2001 BOL 53.8 6.28 79.0 3.0 6.905961
2002 BOL 50.7 6.16 86.0 3.0 7.264992
Chile 1985 CHI 40.8 4.25 80.0 4.0 6.911765
1990 CHI 48.9 6.95 37.0 2.0 8.058824
1995 CHI 51.1 7.34 37.0 2.0 6.877627
1999 CHI 53.3 6.84 56.0 2.0 6.752473
2002 CHI 53.8 6.28 79.0 3.0 6.905961
2003 CHI 50.7 6.16 86.0 3.0 7.264992
I would like to create a for loop that returns a dataframe like this one:
countries change gini change ef
Argentina + +
Bolivia - +
Chile - -
countries
is simply the columns with the country names from the previous dataframe.change gini
should be the percentage difference between the last value of thegini
column for each country and the most recent one. If the percentage increase is positive, then it should show a+
; if it is negativa then it should show a-
.change ef
follows the same logic as thechange gini
in the new dataframe, with the only difference that the values used for calculating the percentage change come from theECONOMIC FREEDOM
column in the original dataframe.
python pandas loops dataframe for-loop
python pandas loops dataframe for-loop
asked Nov 22 '18 at 0:50
Guillermina Sutter SchneiderGuillermina Sutter Schneider
11511
11511
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You can achieve this quite easily via grouping functions.
Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
(Perhaps there is sth wrong with the sample data?)
First group the dataframe by countries and pick just the two columns of interest:
grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']
With this Groupby-Object
you can apply functions to the the subsets of your data which are separated by the grouping feature, countries
in your case.
E.g. to get the last value in each group just ask for
grpd.last()
gini ECONOMIC FREEDOM
countries
Argentina 50.7 6.16
Bolivia 50.7 6.16
Chile 50.7 6.16
or accordingly for the first row per group
grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 40.8 4.25
Bolivia 40.8 4.25
Chile 40.8 4.25
for calculating the percentage of change of the last with respect to the first you could therefore simply write
(grpd.last() - grpd.first()) / grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 0.242647 0.449411
Bolivia 0.242647 0.449411
Chile 0.242647 0.449411
EDIT:
the output can also be formatted, e.g. like:
df_change = (grpd.last() - grpd.first()) / grpd.first()
df_change.applymap(lambda x: str.format('{:+.1f%}', x))
gini ECONOMIC FREEDOM
countries
Argentina +24.3% +44.9%
Bolivia +24.3% +44.9%
Chile +24.3% +44.9%
EDIT2:
for signs only:
df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])
gini ECONOMIC FREEDOM
countries
Argentina + +
Bolivia + +
Chile + +
add a comment |
Create empty lists and append desired values from countries
, gini
, and ECONOMIC FREEDOM
columns for each country.
countries =
gini =
efw =
for i in new_df.index.levels[0]:
countries.append(i)
country = new_df.loc[i]
country = country.reset_index()
x = country.iloc[0].tolist()
y = country.iloc[-1].tolist()
change_g = (((y[2] / x[2]) - 1) * 100)
change_e = (((y[3] / x[3]) - 1) * 100)
gini.append(change_g)
efw.append(change_e)
Then do a for loop. For each number you append a +
or a -
.
g =
e =
for n in gini:
if n > 0:
g.append("+")
g.append("-")
for f in efw:
if f > 0:
e.append("+")
e.append("-")
Then create a dataframe with the lists countries
, g
, and e
.
tuples = list(zip(countries,g,e))
changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])
This reads more like general purpose Python and not pandas-style Python. Considergroupby
and vectorized (non-loop) processing.
– Parfait
Nov 23 '18 at 1:26
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
You can achieve this quite easily via grouping functions.
Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
(Perhaps there is sth wrong with the sample data?)
First group the dataframe by countries and pick just the two columns of interest:
grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']
With this Groupby-Object
you can apply functions to the the subsets of your data which are separated by the grouping feature, countries
in your case.
E.g. to get the last value in each group just ask for
grpd.last()
gini ECONOMIC FREEDOM
countries
Argentina 50.7 6.16
Bolivia 50.7 6.16
Chile 50.7 6.16
or accordingly for the first row per group
grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 40.8 4.25
Bolivia 40.8 4.25
Chile 40.8 4.25
for calculating the percentage of change of the last with respect to the first you could therefore simply write
(grpd.last() - grpd.first()) / grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 0.242647 0.449411
Bolivia 0.242647 0.449411
Chile 0.242647 0.449411
EDIT:
the output can also be formatted, e.g. like:
df_change = (grpd.last() - grpd.first()) / grpd.first()
df_change.applymap(lambda x: str.format('{:+.1f%}', x))
gini ECONOMIC FREEDOM
countries
Argentina +24.3% +44.9%
Bolivia +24.3% +44.9%
Chile +24.3% +44.9%
EDIT2:
for signs only:
df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])
gini ECONOMIC FREEDOM
countries
Argentina + +
Bolivia + +
Chile + +
add a comment |
You can achieve this quite easily via grouping functions.
Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
(Perhaps there is sth wrong with the sample data?)
First group the dataframe by countries and pick just the two columns of interest:
grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']
With this Groupby-Object
you can apply functions to the the subsets of your data which are separated by the grouping feature, countries
in your case.
E.g. to get the last value in each group just ask for
grpd.last()
gini ECONOMIC FREEDOM
countries
Argentina 50.7 6.16
Bolivia 50.7 6.16
Chile 50.7 6.16
or accordingly for the first row per group
grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 40.8 4.25
Bolivia 40.8 4.25
Chile 40.8 4.25
for calculating the percentage of change of the last with respect to the first you could therefore simply write
(grpd.last() - grpd.first()) / grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 0.242647 0.449411
Bolivia 0.242647 0.449411
Chile 0.242647 0.449411
EDIT:
the output can also be formatted, e.g. like:
df_change = (grpd.last() - grpd.first()) / grpd.first()
df_change.applymap(lambda x: str.format('{:+.1f%}', x))
gini ECONOMIC FREEDOM
countries
Argentina +24.3% +44.9%
Bolivia +24.3% +44.9%
Chile +24.3% +44.9%
EDIT2:
for signs only:
df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])
gini ECONOMIC FREEDOM
countries
Argentina + +
Bolivia + +
Chile + +
add a comment |
You can achieve this quite easily via grouping functions.
Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
(Perhaps there is sth wrong with the sample data?)
First group the dataframe by countries and pick just the two columns of interest:
grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']
With this Groupby-Object
you can apply functions to the the subsets of your data which are separated by the grouping feature, countries
in your case.
E.g. to get the last value in each group just ask for
grpd.last()
gini ECONOMIC FREEDOM
countries
Argentina 50.7 6.16
Bolivia 50.7 6.16
Chile 50.7 6.16
or accordingly for the first row per group
grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 40.8 4.25
Bolivia 40.8 4.25
Chile 40.8 4.25
for calculating the percentage of change of the last with respect to the first you could therefore simply write
(grpd.last() - grpd.first()) / grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 0.242647 0.449411
Bolivia 0.242647 0.449411
Chile 0.242647 0.449411
EDIT:
the output can also be formatted, e.g. like:
df_change = (grpd.last() - grpd.first()) / grpd.first()
df_change.applymap(lambda x: str.format('{:+.1f%}', x))
gini ECONOMIC FREEDOM
countries
Argentina +24.3% +44.9%
Bolivia +24.3% +44.9%
Chile +24.3% +44.9%
EDIT2:
for signs only:
df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])
gini ECONOMIC FREEDOM
countries
Argentina + +
Bolivia + +
Chile + +
You can achieve this quite easily via grouping functions.
Unfortunately the first and last values of the three countries in your dataset are the same, so the result is also three times the same two values.
(Perhaps there is sth wrong with the sample data?)
First group the dataframe by countries and pick just the two columns of interest:
grpd = df.groupby('countries')['gini', 'ECONOMIC FREEDOM']
With this Groupby-Object
you can apply functions to the the subsets of your data which are separated by the grouping feature, countries
in your case.
E.g. to get the last value in each group just ask for
grpd.last()
gini ECONOMIC FREEDOM
countries
Argentina 50.7 6.16
Bolivia 50.7 6.16
Chile 50.7 6.16
or accordingly for the first row per group
grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 40.8 4.25
Bolivia 40.8 4.25
Chile 40.8 4.25
for calculating the percentage of change of the last with respect to the first you could therefore simply write
(grpd.last() - grpd.first()) / grpd.first()
gini ECONOMIC FREEDOM
countries
Argentina 0.242647 0.449411
Bolivia 0.242647 0.449411
Chile 0.242647 0.449411
EDIT:
the output can also be formatted, e.g. like:
df_change = (grpd.last() - grpd.first()) / grpd.first()
df_change.applymap(lambda x: str.format('{:+.1f%}', x))
gini ECONOMIC FREEDOM
countries
Argentina +24.3% +44.9%
Bolivia +24.3% +44.9%
Chile +24.3% +44.9%
EDIT2:
for signs only:
df_change.applymap(lambda x: ['-', ' ', '+'][np.sign(x).astype(int)+1])
gini ECONOMIC FREEDOM
countries
Argentina + +
Bolivia + +
Chile + +
edited Nov 22 '18 at 23:48
answered Nov 22 '18 at 6:14
SpghttCdSpghttCd
4,6822313
4,6822313
add a comment |
add a comment |
Create empty lists and append desired values from countries
, gini
, and ECONOMIC FREEDOM
columns for each country.
countries =
gini =
efw =
for i in new_df.index.levels[0]:
countries.append(i)
country = new_df.loc[i]
country = country.reset_index()
x = country.iloc[0].tolist()
y = country.iloc[-1].tolist()
change_g = (((y[2] / x[2]) - 1) * 100)
change_e = (((y[3] / x[3]) - 1) * 100)
gini.append(change_g)
efw.append(change_e)
Then do a for loop. For each number you append a +
or a -
.
g =
e =
for n in gini:
if n > 0:
g.append("+")
g.append("-")
for f in efw:
if f > 0:
e.append("+")
e.append("-")
Then create a dataframe with the lists countries
, g
, and e
.
tuples = list(zip(countries,g,e))
changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])
This reads more like general purpose Python and not pandas-style Python. Considergroupby
and vectorized (non-loop) processing.
– Parfait
Nov 23 '18 at 1:26
add a comment |
Create empty lists and append desired values from countries
, gini
, and ECONOMIC FREEDOM
columns for each country.
countries =
gini =
efw =
for i in new_df.index.levels[0]:
countries.append(i)
country = new_df.loc[i]
country = country.reset_index()
x = country.iloc[0].tolist()
y = country.iloc[-1].tolist()
change_g = (((y[2] / x[2]) - 1) * 100)
change_e = (((y[3] / x[3]) - 1) * 100)
gini.append(change_g)
efw.append(change_e)
Then do a for loop. For each number you append a +
or a -
.
g =
e =
for n in gini:
if n > 0:
g.append("+")
g.append("-")
for f in efw:
if f > 0:
e.append("+")
e.append("-")
Then create a dataframe with the lists countries
, g
, and e
.
tuples = list(zip(countries,g,e))
changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])
This reads more like general purpose Python and not pandas-style Python. Considergroupby
and vectorized (non-loop) processing.
– Parfait
Nov 23 '18 at 1:26
add a comment |
Create empty lists and append desired values from countries
, gini
, and ECONOMIC FREEDOM
columns for each country.
countries =
gini =
efw =
for i in new_df.index.levels[0]:
countries.append(i)
country = new_df.loc[i]
country = country.reset_index()
x = country.iloc[0].tolist()
y = country.iloc[-1].tolist()
change_g = (((y[2] / x[2]) - 1) * 100)
change_e = (((y[3] / x[3]) - 1) * 100)
gini.append(change_g)
efw.append(change_e)
Then do a for loop. For each number you append a +
or a -
.
g =
e =
for n in gini:
if n > 0:
g.append("+")
g.append("-")
for f in efw:
if f > 0:
e.append("+")
e.append("-")
Then create a dataframe with the lists countries
, g
, and e
.
tuples = list(zip(countries,g,e))
changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])
Create empty lists and append desired values from countries
, gini
, and ECONOMIC FREEDOM
columns for each country.
countries =
gini =
efw =
for i in new_df.index.levels[0]:
countries.append(i)
country = new_df.loc[i]
country = country.reset_index()
x = country.iloc[0].tolist()
y = country.iloc[-1].tolist()
change_g = (((y[2] / x[2]) - 1) * 100)
change_e = (((y[3] / x[3]) - 1) * 100)
gini.append(change_g)
efw.append(change_e)
Then do a for loop. For each number you append a +
or a -
.
g =
e =
for n in gini:
if n > 0:
g.append("+")
g.append("-")
for f in efw:
if f > 0:
e.append("+")
e.append("-")
Then create a dataframe with the lists countries
, g
, and e
.
tuples = list(zip(countries,g,e))
changes = pd.DataFrame(tuples, columns=['Country','Change in Gini', "Change in Economic Freedom"])
answered Nov 22 '18 at 2:15
Guillermina Sutter SchneiderGuillermina Sutter Schneider
11511
11511
This reads more like general purpose Python and not pandas-style Python. Considergroupby
and vectorized (non-loop) processing.
– Parfait
Nov 23 '18 at 1:26
add a comment |
This reads more like general purpose Python and not pandas-style Python. Considergroupby
and vectorized (non-loop) processing.
– Parfait
Nov 23 '18 at 1:26
This reads more like general purpose Python and not pandas-style Python. Consider
groupby
and vectorized (non-loop) processing.– Parfait
Nov 23 '18 at 1:26
This reads more like general purpose Python and not pandas-style Python. Consider
groupby
and vectorized (non-loop) processing.– Parfait
Nov 23 '18 at 1:26
add a comment |
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