Nested for loops - help with iterating a variable outside of the main loop

[dm222]

Hi all,

First of all there isn't 27 attachments to this question there are 5.
I have made the question more concise so please do read again.

I am working on my first python project and this is my first time posting in a forum.
I am trying to write a function which plots individual country's inflation data against the years since inflation data was collected for said country (in years) in separate subplots within the same figure.
The issue I am having is either being able to plot the last country's inflation data on all subplots OR all countries inflation data on all subplots.
I am working on a Jupyter Notebook on the Anaconda platform.

QUESTION: How can I change this function so as to plot the first country's inflation data on the first subplot, the second country's inflation data on the second subplot, and so on.
The below code (ATTEMPT 1) returns 4 subplots, each subplot contains the 4 countries inflation data.
ATTEMPT 1

# Subplots function with df_transposed dataframe ISSUE: returns subplots each plotting all selected country data.
# This function takes in two arguments: "df" the DataFrame, and "df_col_range" the range of country columns
# to plot.
# This function uses the dictionary "col_pos" to access columns.
# This code appears to plot p = 1-4, (1)Aruba, (2)Africa Eastern and Southern, (3)Afghanistan, (4)Africa Western 
# and Central.

def global_inf_subplots_func(df, col_range_index):
    # col_pos creates a dictionary so as to be access columns by position (column index)
    col_pos = {df_transposed.columns.get_loc(c): c for idx, c in enumerate(df_transposed.columns)}
    
    x = df_transposed['years']
    
    rows, cols = int(col_range_index/2), int(col_range_index/2)
    
    fig, ax = plt.subplots(rows, cols, figsize = (30, 30),
                       sharex='col', 
                       sharey='row')

    
    for row in range(rows):
        for col in range(cols):
   
            for p in col_pos:
                if p != 0 and p < col_range_index:
                    y = df_transposed.iloc[:, p]
                    ax[row, col].plot(x, y)
                
plt.show()
                
global_inf_subplots_func(df_transposed, 5)

Output:
   

QUESTION: The below attempt (attempt 2) has the same goal (to plot 4 subplots, each subplot containing different country's inflation data. Instead, all 4 subplots have the last country of the 4 selected plotted.
Attempt 2:

# Subplots function with df_transposed dataframe **** on each iteration, same y axis data is being plotted 
# I need the code to add the next iteration of the dataframes columns to be plotted on the next subplot, 
# then move onto the next

# This function takes in four arguments: "df" the DataFrame, and "df_col_range" the range of country columns
# to plot, "rows" the number of rows of subplots the figure needs to hold, "cols" the number of columns of 
# subplots the figure needs to hold.
# This function used the dictionary "col_pos" to access columns.

# This code appears to plot p = 4, (4) Africa Western and Central, ONLY - Explanation????: the first for loop,
# Iterates over p = 1 to 4 and therefore p = 4 is the last return, 
# Therefore p = 4 is used in y = df_transposed.iloc[:, p] and is used in the subsequent subplots????

def global_inf_subplots_func(df, col_range_index, rows, cols):
    
    # col_pos creates a dictionary so as to be access columns by position (column index)
    col_pos = {df_transposed.columns.get_loc(c): c for idx, c in enumerate(df_transposed.columns)}
    
    x = df_transposed['years']
    
    rows, cols = rows, cols
    
    fig, ax = plt.subplots(rows, cols, figsize = (30, 30),
                       sharex='col', 
                       sharey='row')
    
    for p in col_pos:
        if p != 0 and p < col_range_index:
            y = df_transposed.iloc[:, p]
            
        
    for row in range(rows):
        for col in range(cols):
            ax[row, col].plot(x, y)
              
plt.show()
                
global_inf_subplots_func(df_transposed, 5, 2, 2)

Output:
   

QUESTION: Why does this function plot ALL countries inflation data on each subplot, when only the 4 (as previous) were selected.
Attempt 3:

# Subplots function with df_transposed dataframe **** on each iteration, same y axis data is being plotted 
# I need the code to add the next iteration of the dataframes columns to be plotted on the next subplot, 
# then move onto the next

# This function takes in four arguments: "df" the DataFrame, and "df_col_range" the range of country columns
# to plot, "rows" the number of rows of subplots the figure needs to hold, "cols" the number of columns of 
# subplots the figure needs to hold.
# This function used the dictionary "col_pos" to access columns.

# This code appears to plot ALL countries, and therefore all columns
# (eventhough only 4 countries (4 columns) selected from col_pos)

def global_inf_subplots_func(df, col_range_index, rows, cols):
    
    # col_pos creates a dictionary so as to be access columns by position (column index)
    col_pos = {df_transposed.columns.get_loc(c): c for idx, c in enumerate(df_transposed.columns)}
    

    x = df_transposed['years']
    
    rows, cols = rows, cols
    
    fig, ax = plt.subplots(rows, cols, figsize = (30, 30),
                       sharex='col')

    for p in col_pos:
        if p != 0 and p > col_range_index:
            y = df_transposed.iloc[:,p]
            for row in range(rows):
                for col in range(cols):
                    ax[row, col].plot(x, y)

plt.show()
                
global_inf_subplots_func(df_transposed, 5, 2, 2)

Output:
   

Just for information.... I have also tried to achieve the same with a different approach:
Attempt 4:

def plot_global_inf_sub():
    for c in df_transposed['Aruba':'Zimbabwe']:
        n = len(df_transposed.columns) -1
        i = 0
        a = 1
        x = years
        fig, ax = plt.subplots(int(n/2), int(n/2))
        while i >= n:
                ax[i,(i-i)].plot(x, c)
                ax[i, a(i=+1)].plot(x, c)
                y = df_transposed[c]
                plt.plot(x, y)
                plt.show()
        
plot_global_inf_sub()

No output to the above, In[*] occurs.

Attempt 5:

def plot_global_inf_sub():
        n = len(df_transposed.columns)
        x = years
        p = 1
        i = 0
        for c in df_transposed.loc['Aruba':'Zimbabwe']:
            for p in range(n):
                for q in range(n): 
                    if i <= n:
                        m = p(ax(p), ax(q))
                        fig, m = plt.subplots(int(n/2), int(n/2))
                        a = 1
                        y = df_transposed[c]
                        ax[i,(i-i)].plot(x, y)
                        ax[i, a(i=+1)].plot(x, y)
                        plt.show()
        
plot_global_inf_sub()

Error:
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/var/folders/7n/v1x237vx47z_7y3g2_63b64h0000gs/T/ipykernel_6003/2417806721.py in <module>
     16                         plt.show()
     17 
---> 18 plot_global_inf_sub()

/var/folders/7n/v1x237vx47z_7y3g2_63b64h0000gs/T/ipykernel_6003/2417806721.py in plot_global_inf_sub()
      8                 for q in range(n):
      9                     if i <= n:
---> 10                         m = p(ax(p), ax(q))
     11                         fig, m = plt.subplots(int(n/2), int(n/2))
     12                         a = 1

TypeError: 'numpy.ndarray' object is not callable

Notes from script:
# This script attempts to code for a function "global_inf_subplots_func()" the aim of which is to plot multiple
# subplots. Each subplot should plot years 1960 - 2021 on the x axis, against subsequent countries inflation %
# on the y axis.
# The x axis data is from "x = df_transposed['years']", the years column from the transposed DataFrame:
# df_transposed.
# The y axis data is from the col _pos dictionary. This dictionary gives a number as a key and the respective
# countries inflation % data as a value.

# (Aim*) The overall aim of this function is to be able to plot years vs country (n) inflation data,
# each subsequent subplot containing the next countries inflation % data in the col_pos dictionary.

The DataFrame used in this code:

df_transposed

Output:
Country Name	years	Aruba	Africa Eastern and Southern	Afghanistan	Africa Western and Central	Angola	Albania	Andorra	Arab World	United Arab Emirates	...	Virgin Islands (U.S.)	Vietnam	Vanuatu	World	Samoa	Kosovo	Yemen, Rep.	South Africa	Zambia	Zimbabwe
1960	1960	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1.288859	NaN	NaN
1961	1961	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.102374	NaN	NaN
1962	1962	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	2.654867	NaN	NaN	1.246285	NaN	NaN
1963	1963	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	0.689655	NaN	NaN	1.337970	NaN	NaN
1964	1964	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	5.308219	NaN	NaN	2.534973	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017	2017	-1.028282	6.399343	4.975952	1.764635	29.843587	2.060598	NaN	1.966826	1.966826	...	NaN	3.520257	3.084526	2.192010	1.749897	1.488234	NaN	5.184247	6.577312	0.893962
2018	2018	3.626041	4.720811	0.626149	1.784050	19.628608	2.028060	NaN	2.458142	3.068634	...	NaN	3.539628	2.330964	2.438737	4.197459	1.053798	NaN	4.517165	7.494572	10.618866
2019	2019	4.257462	4.120246	2.302373	1.758565	17.081215	1.411091	NaN	1.091848	-1.931081	...	NaN	2.795824	2.762520	2.186902	0.982327	2.675992	NaN	4.120246	9.150316	255.304991
2020	2020	NaN	5.404815	NaN	2.492522	NaN	1.620887	NaN	1.777408	-2.079403	...	NaN	3.220934	5.329351	1.920968	-1.568912	0.198228	NaN	3.210036	15.732585	557.201817
2021	2021	NaN	7.240978	NaN	3.925603	NaN	2.041472	NaN	3.243460	NaN	...	NaN	1.834716	2.343284	3.423629	3.133205	3.353691	NaN	4.611672	22.021234	98.546105