I have a dataframe object:
import pandas as pd
import matplotlib.pyplot as plt
data=pd.DataFrame({'date':['2013-03-04','2013-03-05','2013-03-06','2013-03-07'],'value':[1,1.1,1.2,1.3]})
and I would like to plot value column against date column, I've tried:
plt.plot(pd.to_datetime(data['date']),data['value'])
The x axis is not the date label I've expected. Anyone could help? Thanks!
You can just plot it like that:
data.plot(x='date', y='value')
Related
I am trying to plot two datasets (HIBOR and US_yield). I don't know why the values of HIBOR in the plot are wrong. However, the values are correct when I plot it alone.
import requests
import pandas as pd
import xmltodict
from datetime import datetime
import json
import matplotlib.pyplot as plt
year=2023
us_url=f'https://home.treasury.gov/resource-center/data-chart-center/interest-rates/pages/xml?data=daily_treasury_yield_curve&field_tdr_date_value={year}'
us_data=requests.get(us_url).content
hk_url=f'https://api.hkma.gov.hk/public/market-data-and-statistics/daily-monetary-statistics/daily-figures-interbank-liquidity'
hk_data=requests.get(hk_url).content
# Parase us data
dict_data_us=xmltodict.parse(us_data)
dict_us=dict()
for key in dict_data_us['feed']['entry'][0]['content']['m:properties'].keys():
dict_us[key.replace('d:','')]=[i['content']['m:properties'][key]['#text'] for i in dict_data_us['feed']['entry']]
df_us=pd.DataFrame(dict_us)
df_us['Date']=[datetime.strptime(i, '%Y-%m-%dT%H:%M:%S') for i in df_us['NEW_DATE']]
df_us.set_index('Date', inplace=True)
# Parase hk data hibor
dict_data_hk=json.loads(hk_data)
dict_hk=dict()
for key in dict_data_hk['result']['records'][0].keys():
dict_hk[key]=[i[key] for i in dict_data_hk['result']['records']]
df_hk=pd.DataFrame(dict_hk)
df_hk['Date']=[datetime.strptime(i, '%Y-%m-%d') for i in df_hk['end_of_date']]
df_hk.set_index('Date', inplace=True)
df_hk.sort_index(inplace=True)
plt.plot(df_hk['hibor_fixing_1m'][-40:], label='HIBOR_1M')
plt.plot(df_us['BC_1MONTH'][-40:], label='US_Yield_1M')
plt.legend()
plt.show()
The reason is that the y-axis fields are read as string in your plot. You need to add astype(float) to them as below...
plt.plot(df_us['BC_1MONTH'][-40:].astype(float), label='US_Yield_1M')
plt.plot(df_hk['hibor_fixing_1m'][-40:].astype(float), label='HIBOR_1M')
This will give you the below plot. Hope this is what you are looking for
I have currently started a project where I need to evaluate and plot data using python. The csv-file that I have to plot are structured like this:
date,ch1,ch2,ch3,date2
11:56:20.149766,0.909257531,0.909420371,1.140183687, 13:56:20.149980
11:56:20.154008,0.895447016,0.895601869,1.122751355, 13:56:20.154197
11:56:20.157245,0.881764293,0.881911397,1.105638862, 13:56:20.157404
11:56:20.160590,-0.009178977,-0.000108901,-1.486875653, 13:56:20.160750
11:56:20.190473,-1.473576546,-1.477073431,-1.846657276, 13:56:20.190605
11:56:20.193810,-1.460405469,-1.463766813,-1.8300246, 13:56:20.193933
11:56:20.197139,-1.447362065,-1.450844049,-1.813711882, 13:56:20.197262
11:56:20.200480,-1.434574604,-1.437921286,-1.797878742, 13:56:20.200604
11:56:20.203803,-1.422042727,-1.425382376,-1.782045603, 13:56:20.203926
11:56:20.207136,-1.40951097,-1.412971258,-1.7663728, 13:56:20.207258
11:56:20.210472,-0.436505407,-0.438260257,-0.54675138, 13:56:20.210595
11:56:20.213804,0.953246772,0.953690529,1.19551909, 13:56:20.213921
11:56:20.217136,0.93815738,0.938464701,1.176487565, 13:56:20.217252
11:56:20.220472,0.923707485,0.924006522,1.158255577, 13:56:20.220590
11:56:20.223807,0.909385324,0.909676254,1.140343547, 13:56:20.223922
11:56:20.227132,0.895447016,0.895729899,1.122911215, 13:56:20.227248
11:56:20.230466,0.881892085,0.882039428,1.105798721, 13:56:20.230582
I can already read the file and print it using pandas:
df = pd.read_csv (r'F:\Schule\HTL\Diplomarbeit\aw_python\datei_meas.csv')
print (df)
But now I want to plot the file using matplotlib. The first column date should be in the x axis and column 2,3 and 4 should be the y-values of different graphs.
I hope that anyone can help me with my problem.
Kind regards
Matthias
Edit:
This is what I have tried to convert the date-column into a readable file-format:
import matplotlib.pyplot as plt
import numpy as np
import mplcursors
import pandas as pd
import matplotlib.dates as mdates
df = pd.read_csv (r'F:\Schule\HTL\Diplomarbeit\aw_python\datei_meas.csv')
print (df)
x_list = df.date
y = df.ch1
x = mdates.date2num(x_list)
plt.scatter(x,y)
plt.show
And this is the occurring error message:
d = d.astype('datetime64[us]')
ValueError: Error parsing datetime string " 11:56:20.149766" at position 3
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
corona_data = pd.read_csv("서울시 코로나19 확진자 현황 csv.csv", encoding="cp949")
confirmed_dates = corona_data["확진일"]
confirmed_date = [datetime.strptime(date, "%Y-%m-%d") for date in confirmed_dates]
corona_data["확진일"]= confirmed_date
plt.rc('font', family='Malgun Gothic')
corona_data["확진일"].plot(title="확진일 별 확진자 추이")
plt.show()
This plot show x-axis is just number and y-axis is date but I wanna change x-axis is date and y-axis is number how can I solve it?
If your data is in a dataframe, I recommend using Seaborn to visualize it. It has a great API that allows you to plot elements of your dataframe by referening column names. Here is a toy example:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
# Load data
df = pd.read_csv(...)
# Plot scatter plot
sns.scatter(x='col_1', y='col_2', data=df)
plt.show()
Check out the Seaborn documentation for more
The problem seems to be that your dataframe only contains one dataset which are the dated. You could add a column that contains the row numbers and then select what you want to have on x and y axis by passing the column name to the plot function:
import matplotlib.pyplot as plt
from datetime import datetime
corona_data = pd.read_csv("서울시 코로나19 확진자 현황 csv.csv", encoding="cp949")
confirmed_dates = corona_data["확진일"]
confirmed_date = [datetime.strptime(date, "%Y-%m-%d") for date in confirmed_dates]
corona_data["확진일"]= confirmed_date
# now add the numbers to the dataset
corona_data["numbers"]=[i for i in len(confirmed_dates)]
plt.rc('font', family='Malgun Gothic')
# and tell the plot function that you want "확진일" as x ans "numbers" as y axis
corona_data.plot("확진일","numbers",title="확진일 별 확진자 추이")
plt.show()```
In my dataset I have a categorical column named 'Type'contain(eg.,INVOICE,IPC,IP) and 'Date' column contain dates(eg,2014-02-01).
how can I plot these two.
On x axis I want date
On y axis a line for (eg.INVOCE) showing its trend
enter image description here
Not very sure what you mean by plot and show trend, one ways is to count like #QuangHoang suggested, and plot with a heatmap, something like below. If it is something different, please expand on your question.
import pandas as pd
import numpy as np
import seaborn as sns
dates = pd.date_range(start='1/1/2018', periods=5, freq='3M')[np.random.randint(0,5,20)]
type = np.random.choice(['INVOICE','IPC','IP'],20)
df = pd.DataFrame({'dates':dates ,'type':type})
tab = pd.crosstab(df['type'],df['dates'].dt.strftime('%d-%m-%Y'))
n = np.unique(tab.values)
cmap = sns.color_palette("BuGn_r",len(n))
sns.heatmap(tab,cmap=cmap)
I have a dataframe with 3 variables:
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Approved"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
I want a barplot grouped by the Period column, showing all the values contained in the Observations column and colored with the Result column.
How can I do this?
I tried the sns.barplot, but it joined the values in Observations column in just one bar(mean of the values).
sns.barplot(x='Period',y='Observations',hue='Result',data=df,ci=None)
Plot output
Assuming that you want one bar for each row, you can do as follows:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
result_cat = df["Result"].astype("category")
result_codes = result_cat.cat.codes.values
cmap = plt.cm.Dark2(range(df["Result"].unique().shape[0]))
patches = []
for code in result_cat.cat.codes.unique():
cat = result_cat.cat.categories[code]
patches.append(mpatches.Patch(color=cmap[code], label=cat))
df.plot.bar(x='Period',
y='Observations',
color=cmap[result_codes],
legend=False)
plt.ylabel("Observations")
plt.legend(handles=patches)
If you would like it grouped by the months, and then stacked, please use the following (note I updated your code to make sure one month had more than one status), but not sure I completely understood your question correctly:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Under evaluation"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
df.groupby(['Period', 'Result'])['Observations'].sum().unstack('Result').plot(kind='bar', stacked=True)