How do you reformat from datetime to Week 1, Week 2... to plot onto a seaborn line chart?
Input
Date Ratio
0 2019-10-04 0.350365
1 2019-10-04 0.416058
2 2019-10-11 0.489051
3 2019-10-18 0.540146
4 2019-10-25 0.598540
5 2019-11-08 0.547445
6 2019-11-01 0.722628
7 2019-11-15 0.788321
8 2019-11-22 0.875912
9 2019-11-27 0.948905
Desired output
I was able to cheese it by matching the natural index of the dataframe to the week. I wonder if there's another way to do this.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = {'Date': ['2019-10-04',
'2019-10-04',
'2019-10-11',
'2019-10-18',
'2019-10-25',
'2019-11-08',
'2019-11-01',
'2019-11-15',
'2019-11-22',
'2019-11-27'],
'Ratio': [0.350365,
0.416058,
0.489051,
0.540146,
0.598540,
0.547445,
0.722628,
0.788321,
0.875912,
0.948905]}
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
graph = sns.lineplot(data=df,x='Date',y='Ratio')
plt.show()
# First plot looks bad.
week_mapping = dict(zip(df['Date'].unique(),range(len(df['Date'].unique()))))
df['Week'] = df['Date'].map(week_mapping)
graph = sns.lineplot(data=df,x='Week',y='Ratio')
plt.show()
# This plot looks better, but method seems cheesy.
It looks like your data is already spaced weekly, so you can just do:
df.groupby('Date',as_index=False)['Ratio'].mean().plot()
Output:
You can make a new column with the week number and use that as your x value. This would give you the week of the year. If you want to start your week numbers with 0, just subtract the week number of the first date from the value (see the commented out section of the code)
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from datetime import datetime as dt
data = {'Date': ['2019-10-04',
'2019-10-04',
'2019-10-11',
'2019-10-18',
'2019-10-25',
'2019-11-08',
'2019-11-01',
'2019-11-15',
'2019-11-22',
'2019-11-27'],
'Ratio': [0.350365,
0.416058,
0.489051,
0.540146,
0.598540,
0.547445,
0.722628,
0.788321,
0.875912,
0.948905]}
df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])
# To get the week number of the year
df.loc[:, 'Week'] = df['Date'].dt.week
# Or you can use the line below for the exact output you had
#df.loc[:, 'Week'] = df['Date'].dt.week - (df.sort_values(by='Date').iloc[0,0].week)
graph = sns.lineplot(data=df,x='Week',y='Ratio')
plt.show()
Related
I read this excell sheet (only column of 'DATEHEUREMAX') with pandas using this command:
xdata = read_excel('Data.xlsx', 'Data', usecols=['DATEHEUREMAX'])
now I want to turn this df into a simplify df with only hour:min rounded to 15min up. The main idea is to plot an histogram base on hour:min
Consider the following DataFrame, with a single column, read as datetime (not string):
Dat
0 2019-06-03 12:07:00
1 2019-06-04 10:04:00
2 2019-06-05 11:42:00
3 2019-06-06 10:17:00
To round these dates to 15 mins run:
df['Dat2'] = df.Dat.dt.round('15T').dt.time.map(lambda s: str(s)[:-3])
The result is:
Dat Dat2
0 2019-06-03 12:07:00 12:00
1 2019-06-04 10:04:00 10:00
2 2019-06-05 11:42:00 11:45
3 2019-06-06 10:17:00 10:15
For demonstration purpose, I saved the result in a new column, but you can
save it in the original column.
I think this is what you are asking for
rounded_column = df['time_column'].dt.round('15min').strftime("%H:%M")
although i agree with the commenters you might not really need to do this and just use a timegrouper
There is no need to round your column in order to get a histogram of dates with your DATEHEUREMAX column. For this purpose you can just make use of pd.Grouper as detailed below.
Toy sample code
You can work out this example to get a solution with your date column:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Generating a sample of 10000 timestamps and selecting 500 to randomize them
df = pd.DataFrame(np.random.choice(pd.date_range(start=pd.to_datetime('2015-01-14'),periods = 10000, freq='S'), 500), columns=['date'])
# Setting the date as the index since the TimeGrouper works on Index, the date column is not dropped to be able to count
df.set_index('date', drop=False, inplace=True)
# Getting the histogram
df.groupby(pd.Grouper(freq='15Min')).count().plot(kind='bar')
plt.show()
This code resolves to a graph like below:
Solution with your data
For your data you should be able to do something like:
import pandas as pd
import matplotlib.pyplot as plt
xdata = read_excel('Data.xlsx', 'Data', usecols=['DATEHEUREMAX'])
xdata.set_index('DATEHEUREMAX', drop=False, inplace=True)
xdata.groupby(pd.Grouper(freq='15Min')).count().plot(kind='bar')
plt.show()
I am trying to plot some data, but I don't know how I can add the date values on the x-axis on my graph. Here is my code:
import pandas as pd
import numpy as np
%matplotlib inline
%pylab inline
import matplotlib.pyplot as plt
pylab.rcParams['figure.figsize'] = (15, 9)
df["msft"].plot(grid = True)
The description of the image is a plot, but the x-axis just has numbers, but I am looking for dates to appear on x-axis. The dates are in the date column in the dataframe.
Here is what the dataframe looks like:
date msft nok aapl ibm amzn
1 2018-01-01 09:00:00 112 1 143 130 1298
2 2018-01-01 10:00:00 109 10 185 137 1647
3 2018-01-01 11:00:00 98 11 146 105 1331
4 2018-01-01 12:00:00 83 3 214 131 1355
Can you offer some help on what I am missing?
Your column date is just another column for pandas, you have to tell the program that you want to plot against this specific one. One way is to plot against this column:
from matplotlib import pyplot as plt
import pandas as pd
#load dataframe
df = pd.read_csv("test.txt", delim_whitespace=True)
#convert date column to datetime object, if it is not already one
df["date"] = pd.to_datetime(df["date"])
#plot the specified columns vs dates
df.plot(x = "date", y = ["msft", "ibm"], kind = "line", grid = True)
plt.show()
For more pandas plot options, please have a look at the documentation.
Another way would be to set date as the index of the dataframe. Then you can use your approach:
df.set_index("date", inplace = True)
df[["msft", "ibm"]].plot(grid = True)
plt.show()
The automatic date labels might not be, what you want to display. But there are ways to format the output and you can find examples on SO.
one way to do it is the set_xticklabels function, though Mr. T's answer is the proper way to go
ax = plt.subplot(111)
df["msft"].plot(grid = True)
ax.set_xticklabels(df['date'])
plt.xticks(np.arange(4))
with the data provided:
My DataFrame looks that:
I plot it by this code:
tmp['event_name'].plot(style='.', figsize=(20,10), grid=True)
Results looks that:
I want to change size of points( using column details).
Question:
How can I do it? Plot haven't argument size and I can not using plot.scatter() because I can not use time format for x axis.
DataFrame.plot passes any unknown keywords down to Matplotlib.Artist, as stated in the linked docs. Therefore, you can specify the marker size using the general matplotlib syntax ms:
tmp['event_name'].plot(style='.', figsize=(20,10), grid=True, ms=5)
That said, you can use plt.scatter with time stamps as well, which makes using the 'details' column as marker size more straight forward:
import matplotlib.pyplot as plt
import pandas as pd
data = {'time': ['2015-01-01', '2015-01-02', '2015-01-03', '2015-01-04'],
'event_name': [2, 2, 2, 2],
'details': [46, 16, 1, 7]}
df = pd.DataFrame(data)
dates = [pd.to_datetime(date) for date in df.time]
plt.scatter(dates, df.event_name, s=df.details)
plt.show()
You can try so:
for index, i in enumerate(df['details']):
plt.plot(df.index[index], df.iloc[index]['event_name'], marker='.', linestyle='None', markersize=i*4, color='b')
plt.show()
Example:
import matplotlib.pyplot as plt
import pandas as pd
df = {'time': ['2015-01-01','2015-01-02','2015-01-03', '2015-01-04', '2015-01-05'],'event_name': [2,2,2,2,2], 'details':[46,16,1,7,4]}
df = pd.DataFrame(data=df)
df['time'] = pd.to_datetime(df['time'], format='%Y-%m-%d')
df = df.set_index('time')
df:
details event_name
time
2015-01-01 46 2
2015-01-02 16 2
2015-01-03 1 2
2015-01-04 7 2
2015-01-05 4 2
Output:
Hi I have a dataframe like this:
Date Influenza[it] Febbre[it] Cefalea[it] Paracetamolo[it] \
0 2008-01 989 2395 1291 2933
1 2008-02 962 2553 1360 2547
2 2008-03 1029 2309 1401 2735
3 2008-04 1031 2399 1137 2296
Unnamed: 6 tot_incidence
0 NaN 4.56
1 NaN 5.98
2 NaN 6.54
3 NaN 6.95
I'd like to plot different figures with on x-axis the Date column and the y-axis the Influenza[it] column and another column like Febbre[it]. Then again x-axis the Date column, y-axis Influenza[it] column and another column (ex. Paracetamolo[it]) and so on. I'm trying to figure out if there is a fast way to make it without completely manipulate the dataframes.
You can simply plot 3 different subplots.
import pandas as pd
import matplotlib.pyplot as plt
dic = {"Date" : ["2008-01","2008-02", "2008-03", "2008-04"],
"Influenza[it]" : [989,962,1029,1031],
"Febbre[it]" : [2395,2553,2309,2399],
"Cefalea[it]" : [1291,1360,1401,1137],
"Paracetamolo[it]" : [2933,2547,2735,2296]}
df = pd.DataFrame(dic)
#optionally convert to datetime
df['Date'] = pd.to_datetime(df['Date'])
fig, ax = plt.subplots(1,3, figsize=(13,7))
df.plot(x="Date", y=["Influenza[it]","Febbre[it]" ], ax=ax[0])
df.plot(x="Date", y=["Influenza[it]","Cefalea[it]" ], ax=ax[1])
df.plot(x="Date", y=["Influenza[it]","Paracetamolo[it]" ], ax=ax[2])
#optionally equalize yaxis limits
for a in ax:
a.set_ylim([800, 3000])
plt.show()
If you want to plot each plot separately in a jupyter notebook, the following might do what you want.
Additionally we convert the dates from format year-week to a datetime to be able to plot them with matplotlib.
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
dic = {"Date" : ["2008-01","2008-02", "2008-03", "2008-04"],
"Influenza[it]" : [989,962,1029,1031],
"Febbre[it]" : [2395,2553,2309,2399],
"Cefalea[it]" : [1291,1360,1401,1137],
"Paracetamolo[it]" : [2933,2547,2735,2296]}
df = pd.DataFrame(dic)
#convert to datetime, format year-week -> date (monday of that week)
df['Date'] = [ date + "-1" for date in df['Date']] # add "-1" indicating monday of that week
df['Date'] = pd.to_datetime(df['Date'], format="%Y-%W-%w")
cols = ["Febbre[it]", "Cefalea[it]", "Paracetamolo[it]"]
for col in cols:
plt.close()
fig, ax = plt.subplots(1,1)
ax.set_ylim([800, 3000])
ax.plot(df.Date, df["Influenza[it]"], label="Influenza[it]")
ax.plot(df.Date, df[col], label=col)
ax.legend()
plt.show()
I am plotting several pandas series objects of "total events per week". The data in the series events_per_week looks like this:
Datetime
1995-10-09 45
1995-10-16 63
1995-10-23 83
1995-10-30 91
1995-11-06 101
Freq: W-SUN, dtype: int64
My problem is as follows. All pandas series are the same length, i.e. beginning in same year 1995. One array begins in 2003 however. events_per_week2003 begins in 2003
Datetime
2003-09-08 25
2003-09-15 36
2003-09-22 74
2003-09-29 25
2003-09-05 193
Freq: W-SUN, dtype: int64
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(20,5))
ax = plt.subplot(111)
plt.plot(events_per_week)
plt.plot(events_per_week2003)
I get the following value error.
ValueError: setting an array element with a sequence.
How can I do this?
I really don't get where you're having problems.
I tried to recreate a piece of the dataframe, and it plotted with no problems.
import numpy, matplotlib
data = numpy.array([45,63,83,91,101])
df1 = pd.DataFrame(data, index=pd.date_range('2005-10-09', periods=5, freq='W'), columns=['events'])
df2 = pd.DataFrame(numpy.arange(10,21,2), index=pd.date_range('2003-01-09', periods=6, freq='W'), columns=['events'])
matplotlib.pyplot.plot(df1.index, df1.events)
matplotlib.pyplot.plot(df2.index, df2.events)
matplotlib.pyplot.show()
Using Series instead of Dataframe:
ds1 = pd.Series(data, index=pd.date_range('2005-10-09', periods=5, freq='W'))
ds2 = pd.Series(numpy.arange(10,21,2), index=pd.date_range('2003-01-09', periods=6, freq='W'))
matplotlib.pyplot.plot(ds1)
matplotlib.pyplot.plot(ds2)
matplotlib.pyplot.show()