I have a dataframe like this in a .csv:
Consequence,N_samples
A,227
B,413
C,194
D,1
E,1610
F,10
G,7
H,1
I,1
J,5
K,1
L,5
M,5
N,30
O,7
P,3
And I want to make a plot pie out of it, but grouping all values lower than 150 into "Other" category. I've tried running this code but it's not working.
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plotother = {'Consequence' : 'Other', 'N_samples':0}
df=pd.read_csv('df.csv', sep=',')
df = df.append(other,ignore_index=True)
for i in df:
if (x in df['N_samples']) < 150:
df['N_samples'].iloc[-1]=df['N_samples'].iloc[-1] + (x in df['N_samples'])
df.drop([x])
df.plot.pie(label="", title="Consequence", startangle=90);
plot.savefig('Consequence.svg')
Once I run it I get the following error:
KeyError: "['Consequence'] not found in axis"
I would really appreciate any help.
You are making it more difficult than it is.
First get all the rows, where sample size is below 150:
small_sizes = df[df['N_Samples']<150]
The sum up their values:
other_samples = small_sizes['N_Samples'].sum()
Finally drop the rows and add the other row:
df = df[~df['N_Samples']<150]
df.loc['other','N_samples'] = other_samples
That should do the trick.
you can do this as follows:
import pandas as pd
from matplotlib import pyplot as plt
df = pd.read_csv('df.csv')
collect the rows <150 into a new df:
df_other=pd.DataFrame([{'Consequence':'Other','N_samples':df[df.N_samples<150].N_samples.sum()}])
add that to the rows >= 150 and plot
df2=df[df.N_samples>=150]
df3=pd.concat([df2,df_other],axis=0)
df3.plot.pie(y='N_samples',labels=df3['Consequence'])
plt.show()
if you find yourself iterating thru a dataframe, be aware there's often a built-in way to do whatever you're trying to do.
Define your filtering condition:
cond = df.N_samples < 150
Sum values from filtering condition:
other_sum = df.N_samples[cond].sum()
Filter by opposite to condition and add 'other' row at the bottom in the same line:
df = df.loc[~cond].append({'Consequence': 'other', 'N_samples': other_sum}, ignore_index=True)
Related
Using the yfinance API I pulled data from there option chain object and converted it to a dictionary. I tried to delete all rows that contained "True" in the column labeled "inTheMoney" however when I run the program it does not do so.
import yfinance as yf
import pandas as pd
price = 100
ticker = yf.Ticker("SPY")
opt = ticker.option_chain('2022-11-18')
df = pd.DataFrame(opt.puts)
#df = df.drop(df[(df['inTheMoney'] != 'True')].index)
df = df.drop(['contractSymbol', 'lastTradeDate', 'change', 'percentChange', 'volume', 'openInterest', 'impliedVolatility', 'contractSize', 'currency'], axis = 1)
print(df)
I also tried to use a for loop and loc but that did not work either.
for index in range(len(df)):
#print(df.loc[index, 'strike'])
if df.loc[index, 'strike'] < 100:
print(df.loc[index])
Any help is greatly appreciated
just:
df = df.drop(df[(df['inTheMoney'] != True)].index) #do not use quotes
I want to loop over 2 columns in a specific dataframe and I want to access the data by the name of the column but it gives me this error (type error) on line 3
i=0
for name,value in df.iteritems():
q1=df[name].quantile(0.25)
q3=df[name].quantile(0.75)
IQR=q3-q1
min=q1-1.5*IQR
max=q3+1.5*IQR
minout=df[df[name]<min]
maxout=df[df[name]>max]
new_df=df[(df[name]<max) & (df[name]>min)]
i+=1
if i==2:
break
It looks like you want to exclude outliers based on the 1.5*IQR rule. Here is a simpler solution:
Input dummy data:
import numpy as np
np.random.seed(0)
df = pd.DataFrame({'col%s' % (i+1): np.random.normal(size=1000)
for i in range(4)})
Removing the outliers (keep data: Q1-1.5IQR < data < Q3+1.5IQR):
Q1 = df.iloc[:, :2].quantile(.25)
Q3 = df.iloc[:, :2].quantile(.75)
IQR = Q3-Q1
non_outliers = (df.iloc[:, :2] > Q1-1.5*IQR) & (df.iloc[:, :2] < Q3+1.5*IQR)
new_df = df[non_outliers.all(axis=1)]
output:
Type error might happen for a lot of reasons so it will be better if you add part of the DF to try to understand the issue.
Also to loop over columns you can also use the iterrows() function:
import pandas as pd
df = pd.read_csv('filename.csv')
for _, content in df.iterrows():
print(content['columnname']) #add the name of the columns you want to loop over
refer to the following link for more information
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.iterrows.html#pandas.DataFrame.iterrows
I am trying to make 6 separate graphs from a dataframe that has 5 columns and multiple rows that is imported from Excel. I want to add two lines to the graph that are the point in the dataframe plus and minus the rolling standard deviation at each point in each column and row of the dataframe. To do this I am using a nested for loop and then graphing, however, it is saying wrong number of items pass placement implies 1. I do not know how to fix this.
I have tried converting the dataframe to a list and appending rows as well. Nothing seems to work. I know this could be easily done.
import pandas as pd
import matplotlib.pyplot as plt
excel_file = 'C:/Users/afrydman/Documents/Storage and Data Centers FFO Multiples Data.xlsx'
dfStorage = pd.read_excel(excel_file,sheet_name='Storage Data', index_col='Date')
dfrollingStd = dfStorage.rolling(12).std().shift(-11)
#dfrollingStd.fillna(0)
#print(dfStorage[1][3])
for k,p in dfStorage, dfrollingStd:
dftemp = pd.DataFrame(dfStorage,columns=[k])
dfnew=pd.DataFrame(dfrollingStd,columns=[p])
for i,j in dfStorage, dfrollingStd:
dftemp = pd.DataFrame(dfStorage,index=[i])
dfnew=pd.DataFrame(dfrollingStd,index=[j])
dftemp['-1std'] = pd.DataFrame(dftemp).subtract(dfnew)
dftemp['+1std'] = pd.DataFrame(dftemp).add(dfnew)
pd.DataFrame(dftemp).plot()
plt.ylabel('P/FFO')
I expect the output to be 6 separate graphs each with 3 lines. Instead I am not getting anything. My loop is also not executing properly.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
excel_file = 'C:/Users/afrydman/Documents/Storage and Data Centers FFO Multiples Data.xlsx'
dfStorage = pd.read_excel(excel_file,sheet_name='Storage Data', index_col='Date')
dfrollingStd = dfStorage.rolling(12).std().shift(-11)
#dfrollingStd.fillna(0)
#print(dfStorage[1][3])
for i in dfStorage:
dftemp = pd.DataFrame(dfStorage,columns=[i])
for j in dfrollingStd:
dfnew=pd.DataFrame(dfrollingStd,columns=[j])
dftemp['-1std'] = pd.DataFrame(dftemp).subtract(dfnew)
dftemp['+1std'] = pd.DataFrame(dftemp).add(dfnew)
pd.DataFrame(dftemp).plot()
plt.ylabel('P/FFO')
This is my updated code and I am still getting the same error. This time it is saying Wrong number of items passed 2, placement implies 1
I am trying to do the equivalent of a COUNTIF() function in excel. I am stuck at how to tell the .count() function to read from a specific column in excel.
I have
df = pd.read_csv('testdata.csv')
df.count('1')
but this does not work, and even if it did it is not specific enough.
I am thinking I may have to use read_csv to read specific columns individually.
Example:
Column name
4
4
3
2
4
1
the function would output that there is one '1' and I could run it again and find out that there are three '4' answers. etc.
I got it to work! Thank you
I used:
print (df.col.value_counts().loc['x']
Here is an example of a simple 'countif' recipe you could try:
import pandas as pd
def countif(rng, criteria):
return rng.eq(criteria).sum()
Example use
df = pd.DataFrame({'column1': [4,4,3,2,4,1],
'column2': [1,2,3,4,5,6]})
countif(df['column1'], 1)
If all else fails, why not try something like this?
import numpy as np
import pandas
import matplotlib.pyplot as plt
df = pandas.DataFrame(data=np.random.randint(0, 100, size=100), columns=["col1"])
counters = {}
for i in range(len(df)):
if df.iloc[i]["col1"] in counters:
counters[df.iloc[i]["col1"]] += 1
else:
counters[df.iloc[i]["col1"]] = 1
print(counters)
plt.bar(counters.keys(), counters.values())
plt.show()
I am trying to create an XY chart using Python and the Pygal library. The source data is contained in a CSV file with three columns; ID, Portfolio and Value. Unfortunately I can only plot one axis and I suspect it's an issue with the array. Can anyone point me in the right direction? Do I need to use numpy? Thank you!
import pygal
import pandas as pd
data = pd.read_csv("profit.csv")
data.columns = ["ID", "Portfolio", "Value"]
xy_chart = pygal.XY()
xy_chart.add('Portfolio', data['Portfolio','Value'] << I suspect this is wrong
)
xy_chart.render_in_browser()
With
import pygal
import pandas as pd
data = pd.read_csv("profit.csv")
data.columns = ["ID", "Portfolio", "Value"]
xy_chart = pygal.XY()
xy_chart.add('Portfolio', data['Portfolio']
)
xy_chart.render_in_browser()
I get:
A graph with a series of horizontal data points/values; i.e. it has the X values but no Y values.
With:
import pygal
import pandas as pd
data = pd.read_csv("profit.csv")
data.columns = ["ID", "Portfolio", "Value"]
xy_chart = pygal.XY()
xy_chart.add('Portfolio', data['Portfolio','Value']
)
xy_chart.render_in_browser()
I get:
KeyError: ('Portfolio', 'Value')
Sample data:
ID Portfolio Value
1 1 -2560.042036
2 2 1208.106958
3 3 5702.386949
4 4 -8827.63913
5 5 -3881.665733
6 6 5951.602484
Maybe a little late here, but I just did something similar. Your second example requires multiple columns to be handed in as a array and then the DataFrame you get back needs to be converted into a list of tuples.
import pygal
import pandas as pd
data = pd.read_csv("profit.csv")
data.columns = ["ID", "Portfolio", "Value"]
points = data[['Portfolio','Value']].to_records(index=False).tolist()
xy_chart = pygal.XY()
xy_chart.add('Portfolio', points)
xy_chart.render_in_browser()
There may be a more elegant use of the pandas or pygal API to get the columns into a list of tuples.