Stuck using pandas to build RPG item generator - python-3.x

I am trying to build a simple random item generator for a game I am working on.
So far I am stuck trying to figure out how to store and access all of the data. I went with pandas using .csv files to store the data sets.
I want to add weighted probabilities to what items are generated so I tried to read the csv files and compile each list into a new set.
I got the program to pick a random set but got stuck when trying to pull a random row from that set.
I am getting an error when I use .sample() to pull the item row which makes me think I don't understand how pandas works. I think I need to be creating new lists so I can later index and access the various statistics of the items once one is selected.
Once I pull the item I was intending on adding effects that would change the damage and armor and such displayed. So I was thinking of having the new item be its own list then use damage = item[2] + 3 or whatever I need
error is: AttributeError: 'list' object has no attribute 'sample'
Can anyone help with this problem? Maybe there is a better way to set up the data?
here is my code so far:
import pandas as pd
import random
df = [pd.read_csv('weapons.csv'), pd.read_csv('armor.csv'), pd.read_csv('aether_infused.csv')]
def get_item():
item_class = [random.choices(df, weights=(45,40,15), k=1)] #this part seemed to work. When I printed item_class it printed one of the entire lists at the correct odds
item = item_class.sample()
print (item) #to see if the program is working
get_item()

I think you are getting slightly confused with lists vs list elements. This should work. I stubbed your dfs with simple ones
import pandas as pd
import random
# Actual data. Comment it out if you do not have the csv files
df = [pd.read_csv('weapons.csv'), pd.read_csv('armor.csv'), pd.read_csv('aether_infused.csv')]
# My stubs -- uncomment and use this instead of the line above if you want to run this specific example
# df = [pd.DataFrame({'weapons' : ['w1','w2']}), pd.DataFrame({'armor' : ['a1','a2', 'a3']}), pd.DataFrame({'aether' : ['e1','e2', 'e3', 'e4']})]
def get_item():
# I removed [] from the line below -- choices() already returns a list of length 1
item_class = random.choices(df, weights=(45,40,15), k=1)
# I added [0] to choose the first element of item_class which is a list of length 1 from the line above
item = item_class[0].sample()
print (item) #to see if the program is working
get_item()
prints random rows from random dataframes that I setup such as
weapons
1 w2

Related

Nested loops altering rows in pandas - Avoiding "A value is trying to be set on a copy of a slice from a DataFrame"

Summary
I am trying to loop through a pandas dataframe, and to run a secondary loop at each iteration. The secondary loop calculates something that I want to append into the original dataframe, so that when the primary loop advances, some of the rows are recalculated based on the changed values. (For those interested, this is a simple advective model of carbon accumulation in soils. When a new layer of soil is deposited, mixing processes penetrate into older layers and transform their properties to a set depth. Thus, each layer deposited changes those below it incrementally, until a former layer lies below the mixing depth.)
I have produced an example of how I want this to work, however it is generating the common error message:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
self._setitem_single_block(indexer, value, name)
I have looked into the linked information in the error message as well as myriad posts on this forum, but none get into the continual looping through a changed dataframe.
What I've tried, and some possible solutions
Below is some example code. This code works more or less as well as I want it to. But it produces the warning. Should I:
Suppress the warning and continue working with this architecture? In this case, am I asking for trouble with un-reproducible results?
Try a different architecture altogether, like a numpy array from the original dataframe?
Try df.append() or df.copy() to avoid the warning?
I have tried `df.copy()' to no avail - the warning was still thrown.
Example code:
import pandas as pd
a = pd.DataFrame(
{
'a':[x/2 for x in range(1,11)],
'b':['hot dog', 'slider', 'watermelon', 'funnel cake', 'cotton candy', 'lemonade', 'fried oreo', 'ice cream', 'corn', 'sausage'],
'c':['meat', 'meat', 'vegan', 'vegan', 'vegan', 'vegan', 'dairy','dairy', 'vegan', 'meat']
}
)
print(a)
z = [x/(x+2) for x in range(1,5)]
print(z)
#Primary loop through rows of the main dataframe
for ind, row in a.iterrows():
#Pull out a chunk of the dataframe. This is the portion of the dataframe that will be modified. What is below
#this is already modified and locked into the geological record. What is above has not yet been deposited.
b = a.iloc[ind:(ind+len(z)), :]
#Define the size of the secondary loop. Taking the minimum avoids the model mixing below the boundary layer (key error)
loop = min([len(z), len(b)])
#Now loop through the sub-dataframe and change accordingly.
for fraction in range(loop):
b['a'].iloc[fraction] = b['a'].iloc[fraction]*z[fraction]
#Append the original dataframe with new data:
a.iloc[ind:(ind+loop), :] = b
#Try df.copy(), but still throws warning!
#a.iloc[ind:(ind+loop), :] = b.copy()
print(a)

Does anyone know how to pull averages from a text file for several different people?

I have a text file (Player_hits.text) that I am trying to pull player batting averages from. Similar to lines 179-189 I want to find an average. However, I do not want to find the average for the entire team. Instead, I want to find the average of every individual player on the team.
For instance, the text file is set up as such:
Player_hits.txt
In this file a 1 defines a hit and a 0 means the player did not get a hit. I am trying to pull an individual average for both players. (Alex = 0.500, Riley = 0.666)
If someone could help, that would be greatly appreciated!
Thanks!
Link to original code on repl.it: Baseball Stat-Tracking
JSONDecodeError Image
The json.decoder.JSONDecodeError: is coming because the json.loads() doesn't interpret that (each line, '[[1, 'Riley']\n'as valid json format. You can use ast to read in that list as a literal evaluation, thus storing that as a list element [', 'Riley'] in your list of p_hits.
Then the second part is you can convert to the dataframe and groupby the 'name' column. So jim has the right idea, but there's errors in that too (Ie. colmuns should be columns, and the items in the list need to be strings ['hit','name'], not undeclared variables.
import pandas as pd
import ast
p_hits = []
with open('Player_hits.txt') as hits:
for line in hits:
l = ast.literal_eval(line)
p_hits.append(l)
df = pd.DataFrame(p_hits, columns=['hit', 'name'])
Output: with an example dataset I made
print(df.groupby(['name']).mean())
hit
name
Matt 0.714286
Riley 0.285714
Todd 0.500000
import pandas as pd
import json
p_hits = []
with open('Player_hits.txt') as hits:
for line in hits:
l = json.loads(line)
p_hits.append(l)
df = pd.DataFrame.from_records(p_hits, colmuns=[hit, name])
df.groupby(['name']).mean()

Pandas - iterating to fill values of a dataframe

I'm trying to build a data-frame of time series data. I have to retrieve the data from an API and every (i,j) entry in the data-frame (where "i" is the row and "j" is the column) has to be iterated through and filled individually.
Here's an idea of the type of thing i'm trying to do (note the API i'm using doesn't have historical data for what i'm trying to analyze):
import pandas as pd
import numpy as np
import time
def retrievedata(string):
take string
do some stuff with api
return float
label_list = ['label1','label1','label1', etc...]
discrete_points = 720
df = pd.DataFrame(index=np.arange(0, discrete_points), columns=(i for i in label_list))
So at this point I've pre-allocated a data frame. What comes next is the issue.
Now, I want to iterate over it and assign values to every (i,j) entry in the data frame based on a function i wrote to pull data. Note that the function I wrote has to be specific to a certain column (as it is taking as input the column label). And on top of that, each row will have different values b/c it is time-series data.
EDIT: Yuck, I found a gross way to make it work:
for row in range(discrete_points):
for label in label_list:
df.at[row, label] = retrievedata(label)
This is obviously a non-pythonic, non-numpy, non-pandas way of doing things. So i'd like to find a nicer and more efficient/less computing power intensive way of doing this.
I'm assuming it's gonna have to be some combination of: iter.rows(); iter.tuples(); df.loc(); df.at()
I'm stumped though.
Any ideas?

Adding numerical values from dict to a new column in a Pandas DataFrame

I am practicing machine learning and working with a movie/rating dataset. I am trying to create a new column in the dataframe which numerically identifies each genre (around 1300 of them). My logic was to create a dictionary of the unique genres and label with a integer. Then create a for loop to iterate through each row of the dataframe, checking the genre of each, then assigning its appropriate value to a new column named "genre_Id". However this has been causing a infinite loop in which I can not even break with ctrl-c. Same issue when working in Jupyter ( Interrupt Kernel fails to stop it). Below is a summarized version of my approach.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
movies_data = pd.read_csv("C://mypython/moviedata/movies.csv")
ratings_data = pd.read_csv("C://mypython/moviedata/ratings.csv")
joined = pd.merge(movies_data,ratings_data, how = 'inner', on=['movieId'])
print(joined.head())
pd.options.display.float_format = '{:,.2f}'.format
genres = joined['genres'].unique()
genre_dict = {}
Id = 1
for i in genres:
genre_dict[i] = Id
Id += 1
joined['genre_id'] = 0
increment = 0
for i in joined['genres']:
if i in genre_dict:
joined['genre_id'][increment] = genre_dict[i]
increment += 1
I know I should probably be taking a smaller sample to work with as there is about 20,000,000 rows in the dataset but I figured I'd try this as a exercise.
I also recieve the "setting values from copy warning" though this hasn't caused me issues in the past for my other projects. Any thoughts on how to do this would be greatly appreciated.
EDIT Found a solution using the Series map feature.
joined['genre_id'] = joined.genres.map(genre_dict)
I have no permission to just comment. This is a suggestion and right procedure to handle categorical values in a dataset. You can use inbuilt sklearn.preprocessing.OneHotEncoder function which do the work you wanted to do.
For better understanding with examples check this One Hot Encode Sequence Data in Python. Let me know if this works for you.

Abaqus Python script -- Reading 'TENSOR_3D_FULL' data from *.odb file

What I want: strain values LE11, LE22, LE12 at nodal points
My script is:
#!/usr/local/bin/python
# coding: latin-1
# making the ODB commands available to the script
from odbAccess import*
import sys
import csv
odbPath = "my *.odb path"
odb = openOdb(path=odbPath)
assembly = odb.rootAssembly
# count the number of frames
NumofFrames = 0
for v in odb.steps["Step-1"].frames:
NumofFrames = NumofFrames + 1
# create a variable that refers to the reference (undeformed) frame
refFrame = odb.steps["Step-1"].frames[0]
# create a variable that refers to the node set ‘Region Of Interest (ROI)’
ROINodeSet = odb.rootAssembly.nodeSets["ROI"]
# create a variable that refers to the reference coordinate ‘REFCOORD’
refCoordinates = refFrame.fieldOutputs["COORD"]
# create a variable that refers to the coordinates of the node
# set in the test frame of the step
ROIrefCoords = refCoordinates.getSubset(region=ROINodeSet,position= NODAL)
# count the number of nodes
NumofNodes =0
for v in ROIrefCoords.values:
NumofNodes = NumofNodes +1
# looping over all the frames in the step
for i1 in range(NumofFrames):
# create a variable that refers to the current frame
currFrame = odb.steps["Step-1"].frames[i1+1]
# looping over all the frames in the step
for i1 in range(NumofFrames):
# create a variable that refers to the strain 'LE'
Str = currFrame.fieldOutputs["LE"]
ROIStr = Str.getSubset(region=ROINodeSet, position= NODAL)
# initialize list
list = [[]]
# loop over all the nodes in each frame
for i2 in range(NumofNodes):
strain = ROIStr.values [i2]
list.insert(i2,[str(strain.dataDouble[0])+";"+str(strain.dataDouble[1])+\
";"+str(strain.dataDouble[3]))
# write the list in a new *.csv file (code not included for brevity)
odb.close()
The error I get is:
strain = ROIStr.values [i2]
IndexError: Sequence index out of range
Additional info:
Details for ROIStr:
ROIStr.name
'LE'
ROIStr.type
TENSOR_3D_FULL
OIStr.description
'Logarithmic strain components'
ROIStr.componentLabels
('LE11', 'LE22', 'LE33', 'LE12', 'LE13', 'LE23')
ROIStr.getattribute
'getattribute of openOdb(r'path to .odb').steps['Step-1'].frames[1].fieldOutputs['LE'].getSubset(position=INTEGRATION_POINT, region=openOdb(r'path to.odb').rootAssembly.nodeSets['ROI'])'
When I use the same code for VECTOR objects, like 'U' for nodal displacement or 'COORD' for nodal coordinates, everything works without a problem.
The error happens in the first loop. So, it is not the case where it cycles several loops before the error happens.
Question: Does anyone know what is causing the error in the above code?
Here the reason you get an IndexError. Strains are (obviously) calculated at the integration points; according to the ABQ Scripting Reference Guide:
A SymbolicConstant specifying the position of the output in the element. Possible values are:
NODAL, specifying the values calculated at the nodes.
INTEGRATION_POINT, specifying the values calculated at the integration points.
ELEMENT_NODAL, specifying the values obtained by extrapolating results calculated at the integration points.
CENTROID, specifying the value at the centroid obtained by extrapolating results calculated at the integration points.
In order to use your code, therefore, you should get the results using position= ELEMENT_NODAL
ROIrefCoords = refCoordinates.getSubset(region=ROINodeSet,position= ELEMENT_NODAL)
With
ROIStr.values[0].data
You will then get an array containing the 6 independent components of your tensor.
Alternative Solution
For reading time series of results for a nodeset, you can use the function xyPlot.xyDataListFromField(). I noticed that this function is much faster than using odbread. The code also is shorter, the only drawback is that you have to get an abaqus license for using it (in contrast to odbread which works with abaqus python which only needs an installed version of abaqus and does not need to get a network license).
For your application, you should do something like:
from abaqus import *
from abaqusConstants import *
from abaqusExceptions import *
import visualization
import xyPlot
import displayGroupOdbToolset as dgo
results = session.openOdb(your_file + '.odb')
# without this, you won't be able to extract the results
session.viewports['Viewport: 1'].setValues(displayedObject=results)
xyList = xyPlot.xyDataListFromField(odb=results, outputPosition=NODAL, variable=((
'LE', INTEGRATION_POINT, ((COMPONENT, 'LE11'), (COMPONENT, 'LE22'), (
COMPONENT, 'LE33'), (COMPONENT, 'LE12'), )), ), nodeSets=(
'ROI', ))
(Of course you have to add LE13 etc.)
You will get a list of xyData
type(xyList[0])
<type 'xyData'>
Containing the desired data for each node and each output. It size will therefore be
len(xyList)
number_of_nodes*number_of_requested_outputs
Where the first number_of_nodes elements of the list are the LE11 at each nodes, then LE22 and so on.
You can then transform this in a NumPy array:
LE11_1 = np.array(xyList[0])
would be LE11 at the first node, with dimensions:
LE.shape
(NumberTimeFrames, 2)
That is, for each time step you have time and output variable.
NumPy arrays are also very easy to write on text files (check out numpy.savetxt).

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