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print('Dropping the non relevant fields') | |
print('Shape before dropping the fields {}'.format(df_state.shape)) | |
df_state = df_state.drop(df_state.index[df_state.State == 'Total (All India)']) | |
df_state = df_state.drop(df_state.index[df_state.State == 'Total (States)']) | |
df_state = df_state.drop(df_state.index[df_state.State == 'Total (Uts)']) | |
print('Shape after dropping the fields {}'.format(df_state.shape)) |
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# Groupby states | |
df_state = df.drop('Year', axis=1) | |
df_state = df_state.groupby('State', as_index=False).agg({'Total': 'sum'}) | |
print(df_state.head()) |
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import pandas as pd | |
data = "./suicides.csv" | |
df = pd.read_csv(data) | |
print(df.head()) |
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%matplotlib inline | |
import networkx as nx | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import pylab | |
# initialise a directed graph | |
G = nx.DiGraph() |
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graph = {'a': {'w': 14, 'x': 7, 'y': 9}, | |
'b': {'w': 9, 'z': 6}, | |
'w': {'a': 14, 'b': 9, 'y': 2}, | |
'x': {'a': 7, 'y': 10, 'z': 15}, | |
'y': {'a': 9, 'w': 2, 'x': 10, 'z': 11}, | |
'z': {'b': 6, 'x': 15, 'y': 11}} |
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findapath(X, Y, W, [X,Y], _) :- edge(X, Y, W). % findapath between X and Y has weight W if there is an edge between X and Y of weight W | |
findapath(X, Y, W, [X|P], V) :- % else findapath between X and Y | |
\+ member(X, V), % is true | |
edge(X, Z, W1), % if we can findapath between X and Z with weight W1 | |
findapath(Z, Y, W2, P, [X|V]), % and there is findapat between Z and Y of weight W2 | |
W is W1 + W2. % where W is W1 + W2 | |
:-dynamic(solution/2). | |
findminpath(X, Y, W, P) :- | |
\+ solution(_, _), |
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edge(a,w,14). | |
edge(a,x,7). | |
edge(a,y,9). | |
edge(b,w,9). | |
edge(b,z,6). | |
edge(w,a,14). | |
edge(w,b,9). | |
edge(w,y,2). |
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main :- | |
has(X,apples), % who has apples | |
has(Y,plums), % who has plums | |
write(X), % write the apple lovers | |
put(10), % new line | |
write(Y), % write the plum lovers | |
nl, halt. % new line and halt |
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main :- | |
listing(fruit), nl, halt. |