Using code from David Dale (Time Wheel in python3 pandas), my data is fairly large but has a few hours that don't have data and subsequently the corresponding wedges are not shown in the time wheel. And so the wheel is missing wedges and it makes it look wrong even though it technically is not wrong.
I have searched the proposed questions when asking this question and tried to understand the code to alter it but cannot.
David Dale code copied from the link:
def pie_heatmap(table, cmap=cm.hot, vmin=None, vmax=None,inner_r=0.25, pie_args={}):
n, m = table.shape
vmin= table.min().min() if vmin is None else vmin
vmax= table.max().max() if vmax is None else vmax
centre_circle = plt.Circle((0,0),inner_r,edgecolor='black',facecolor='white',fill=True,linewidth=0.25)
plt.gcf().gca().add_artist(centre_circle)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cmapper = cm.ScalarMappable(norm=norm, cmap=cmap)
for i, (row_name, row) in enumerate(table.iterrows()):
labels = None if i > 0 else table.columns
wedges = plt.pie([1] * m,radius=inner_r+float(n-i)/n, colors=[cmapper.to_rgba(x) for x in row.values],
labels=labels, startangle=90, counterclock=False, wedgeprops={'linewidth':-1}, **pie_args)
plt.setp(wedges[0], edgecolor='white',linewidth=1.5)
wedges = plt.pie([1], radius=inner_r+float(n-i-1)/n, colors=['w'], labels=[row_name], startangle=-90, wedgeprops={'linewidth':0})
plt.setp(wedges[0], edgecolor='white',linewidth=1.5)
The code works well - thanks Dave - but I need it to make the time wheel with 24 wedges regardless of whether data exists or not for that wedge. Thanks for any help!
Update: I wrote a script to make it work for me. data is my 7x24 data table (7 days by 24 hours each). hours is a list of the 24 hours of the day. ie, ['00:00','01:00','02:00','03:00' ... '23:00']. It is a list of strings because the data is strings when we get it.
blankhours=pandas.DataFrame(0,index=np.arange(0,24),columns=np.arange(1)) #to get 0s
shape=data.shape
if shape[1] < 24: #to access shape of the columns
for h in hours:
hour=0 #counter
if h not in data.columns.values: #see if it is in what should be the complete list
data.insert(hour,h,blankhours) #insert it in there since it wasn't in there
hour+=1 #increment counter
data=data.sort_index(axis=1) #sort the final dataframe by column headers
Hopefully that helps someone...
Related
I am trying to get my head around the Pandas module and started learning about the Series data structure.
I have created the following Series in Spyder :-
songs = pd.Series(data = [145,142,38,13], name = "Count")
I can obtain information about the Series index using the code:-
songs.index
The output of the above code is as follows:-
My question is where it states Start = 0 and Stop = 4, what are these referring to?
I have interpreted start = 0 as the first element in the Series is in row 0.
But i am not sure what Stop value refers to as there are no elements in row 4 of the Series?
Can some one explain?
Thank you.
This concept as already explained adequately in the comments (indexing is at minus one the count of items) is prevalent in many places.
For instance, take the list data structure-
z = songs.to_list()
[145, 142, 38, 13]
len(z)
4 # length is four
# however indexing stops at i-1 position 'i' being the length/count of items in the list.
z[4] # this will raise an IndexError
# you will have to start at index 0 going till only index 3 (i.e. 4 items)
z[0], z[1], z[2], z[-1] # notice how -1 can be used to directly access the last element
I'm sorry, I know this is basic but I've tried to figure it out myself for 2 days by sifting through documentation to no avail.
My code:
import numpy as np
import pandas as pd
name = ["bob","bobby","bombastic"]
age = [10,20,30]
price = [111,222,333]
share = [3,6,9]
list = [name,age,price,share]
list2 = np.transpose(list)
dftest = pd.DataFrame(list2, columns = ["name","age","price","share"])
print(dftest)
name age price share
0 bob 10 111 3
1 bobby 20 222 6
2 bombastic 30 333 9
Want to divide all elements in 'price' column with all elements in 'share' column. I've tried:
print(dftest[['price']/['share']]) - Failed
dftest['price']/dftest['share'] - Failed, unsupported operand type
dftest.loc[:,'price']/dftest.loc[:,'share'] - Failed
Wondering if I could just change everything to int or float, I tried:
dftest.astype(float) - cant convert from str to float
Ive tried iter and items methods but could not understand the printouts...
My only suspicion is to use something called iterate, which I am unable to wrap my head around despite reading other old posts...
Please help me T_T
Apologies in advance for the somewhat protracted answer, but the question is somewhat unclear with regards to what exactly you're attempting to accomplish.
If you simply want price[0]/share[0], price[1]/share[1], etc. you can just do:
dftest['price_div_share'] = dftest['price'] / dftest['share']
The issue with the operand types can be solved by:
dftest['price_div_share'] = dftest['price'].astype(float) / dftest['share'].astype(float)
You're getting the cant convert from str to float error because you're trying to call astype(float) on the ENTIRE dataframe which contains string columns.
If you want to divide each item by each item, i.e. price[0] / share[0], price[1] / share[0], price[2] / share[0], price[0] / share[1], etc. You would need to iterate through each item and append the result to a new list. You can do that pretty easily with a for loop, although it may take some time if you're working with a large dataset. It would look something like this if you simply want the result:
new_list = []
for p in dftest['price'].astype(float):
for s in dftest['share'].astype(float):
new_list.append(p/s)
If you want to get this in a new dataframe you can simply save it to a new dataframe using pd.Dataframe() method:
new_df = pd.Dataframe(new_list, columns=[price_divided_by_share])
This new dataframe would only have one column (the result, as mentioned above). If you want the information from the original dataframe as well, then you would do something like the following:
new_list = []
for n, a, p in zip(dftest['name'], dftest['age'], dftest['price'].astype(float):
for s in dftest['share'].astype(float):
new_list.append([n, a, p, s, p/s])
new_df = pd.Dataframe(new_list, columns=[name, age, price, share, price_div_by_share])
If you check the data types of your dataframe, you will realise that they are all strings/object type :
dftest.dtypes
name object
age object
price object
share object
dtype: object
first step will be to change the relevant columns to numbers - this is one way:
dftest = dftest.set_index("name").astype(float)
dftest.dtypes
age float64
price float64
share float64
dtype: object
This way you make the names a useful index, and separate it from the numeric data. This is just a suggestion; you may have other reasons to leave names as a columns - in that case, you have to individually change the data types of each column.
Once that is done, you can safely execute your code :
dftest.div(dftest.share,axis=0)
age price share
name
bob 3.333333 37.0 1.0
bobby 3.333333 37.0 1.0
bombastic 3.333333 37.0 1.0
I assume this is what you expect as your outcome. If not, you can tweak it. Main part is get your data types as numbers before computation/division can occur.
I have a dataset that indicates date & time in 5-digit format: ddd + hm
ddd part starts from 2009 Jan 1. Since the data was collected only from then to 2-years period, its [min, max] would be [1, 365 x 2 = 730].
Data is observed in 30-min interval, making 24 hrs per day period to lengthen to 48 at max. So [min, max] for hm at [1, 48].
Following is the excerpt of daycode.csv file that contains ddd part of the daycode, matching date & hm part of the daycode, matching time.
And I think I agreed to not showing the dataset which is from ISSDA. So..I will just describe that the daycode in the File1.txt file reads like '63317'.
This link gave me a glimpse of how to approach this problem, and I was in the middle of putting up this code together..which of course won't work at this point.
consume = pd.read_csv("data/File1.txt", sep= ' ', encoding = "utf-8", names =['meter', 'daycode', 'val'])
df1= pd.read_csv("data/daycode.csv", encoding = "cp1252", names =['code', 'print'])
test = consume[consume['meter']==1048]
test['daycode'] = test['daycode'].map(df1.set_index('code')['print'])
plt.plot(test['daycode'], test['val'], '.')
plt.title('test of meter 1048')
plt.xlabel('daycode')
plt.ylabel('energy consumption [kWh]')
plt.show()
Not all units(thousands) have been observed at full length but 730 x 48 is a large combination to lay out on excel by hand. Tbh, not an elegant solution but I tried by dragging - it doesn't quite get it.
If I could read the first 3 digits of the column values and match with another file's column, 2 last digits with another column, then combine.. is there a way?
For the last 2 lines you can just do something like this
df['first_3_digits'] = df['col1'].map(lambda x: str(x)[:3])
df['last_2_digits'] = df['col1'].map(lambda x: str(x)[-2:])
for joining 2 dataframes
df3 = df.merge(df2,left_on=['first_3_digits','last_2_digits'],right_on=['col1_df2','col2_df2'],how='left')
I have the start and end times of trips made by a bus, with the times in an Excel sheet. I want to make the graph as below :
I tried with Matlab nodes and graphs but did not got the exact figure, below is the Matlab code which I tried as an example:
A = [1 4]
B = [2 3]
weights = [5 5];
G = digraph(A,B,weights,4)
plot(G)
And the figure it generates:
I have got many more than 4 points in the Excel sheet, and I want them to all be displayed as in the first image.
Overview
You don't need any sort of complicated graph package for this, just use normal line plots! Here are methods in Excel and Matlab.
Excel
Give each bus stop a number, and list the bus stop number by the time it arrives/leaves there. I'll use stops number 0 and 1 for this example.
0 04:41
1 05:35
1 05:40
0 06:34
0 06:51
1 07:45
1 15:21
0 16:15
Then simply highlight the data and insert a "scatter with straight lines"
The rest is formatting. You can format the y-axis and tick "values in reverse order" to get the time increasing as in your desired plot. You can change the x-axis tick marks to just show integer stop numbers, get rid of the legend etc.
Final output:
Matlab
Here is the Matlab documentation for converting Excel formatted dates into Matlab datetime arrays: Convert Excel Date Number to Datetime.
Once you have the datetime objects, you can do this easily with the standard plot function.
% Set times up as a datetime array, could do this any number of ways
times = datetime(strcat({'1/1/2000 '}, {'04:41', '05:35', '05:40', '06:34', '06:51', '07:45', '15:21', '16:15'}, ':00'), 'format', 'dd/MM/yyyy HH:mm:ss');
% Set up the location of the bus at each of the above times
station = [0,1,1,0,0,1,1,0];
% Plot
plot(station, times) % Create plot
set(gca, 'xtick', [0,1]) % Limit to just ticks at the 2 stops
set(gca, 'ydir', 'reverse') % Reverse y axis to have earlier at top
set(gca,'XTickLabel',{'R', 'L'}) % Name the stops
Output:
I have some irregularly stamped time series data, with timestamps and the observations at every timestamp, in pandas. Irregular basically means that the timestamps are uneven, for instance the gap between two successive timestamps is not even.
For instance the data may look like
Timestamp Property
0 100
1 200
4 300
6 400
6 401
7 500
14 506
24 550
.....
59 700
61 750
64 800
Here the timestamp is say seconds elapsed since a chose origin time. As you can see we could have data at the same timestamp, 6 secs in this case. Basically the timestamps are strictly different, just that second resolution cannot measure the change.
Now I need to shift the timeseries data ahead, say I want to shift the entire data by 60 secs, or a minute. So the target output is
Timestamp Property
0 750
1 800
So the 0 point got matched to the 61 point and the 1 point got matched to the 64 point.
Now I can do this by writing something dirty, but I am looking to use as much as possible any inbuilt pandas feature. If the timeseries were regular, or evenly gapped, I could've just used the shift() function. But the fact that the series is uneven makes it a bit tricky. Any ideas from Pandas experts would be welcome. I feel that this would be a commonly encountered problem. Many thanks!
Edit: added a second, more elegant, way to do it. I don't know what will happen if you had a timestamp at 1 and two timestamps of 61. I think it will choose the first 61 timestamp but not sure.
new_stamps = pd.Series(range(df['Timestamp'].max()+1))
shifted = pd.DataFrame(new_stamps)
shifted.columns = ['Timestamp']
merged = pd.merge(df,shifted,on='Timestamp',how='outer')
merged['Timestamp'] = merged['Timestamp'] - 60
merged = merged.sort(columns = 'Timestamp').bfill()
results = pd.merge(df,merged, on = 'Timestamp')
[Original Post]
I can't think of an inbuilt or elegant way to do this. Posting this in case it's more elegant than your "something dirty", which is I guess unlikely. How about:
lookup_dict = {}
def assigner(row):
lookup_dict[row['Timestamp']] = row['Property']
df.apply(assigner, axis=1)
sorted_keys = sorted(lookup_dict.keys)
df['Property_Shifted'] = None
def get_shifted_property(row,shift_amt):
for i in sorted_keys:
if i >= row['Timestamp'] + shift_amt:
row['Property_Shifted'] = lookup_dict[i]
return row
df = df.apply(get_shifted_property, shift_amt=60, axis=1)