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I have a dataframe that looks like this:
commits commitdates Age (in days) Year-Month server_version
0 97 2021-04-07 75 days 2021-04 v1
1 20 2021-05-31 43 days 2021-05 v3
2 54 2021-06-21 54 days 2021-06 v0.1
3 100 2021-06-18 75 days 2021-06 v2.1.0
4 12 2020-12-06 22 days 2020-12 Nan
I want to plot the Age(in days) which is of type timedelta64[ns] , by the number of commits of type int64 by the server_versions on the x axis.
I tried to do this using plotly, but the age values are a bit strange and don't seem correct, I just want them to be displayed starting from 0 to all the rest, like normal int values, I am not sure how can I fix this.
My code is this:
import plotly.express as px
fig = px.scatter(final_api, x="Version", y="Age (in days)", color="commits", width =1000, height = 800)
fig.show()
I am a bit new to plotly, any help will be appreciated.
All you have to do is to convert your timedelta64 to days and add days as suffix for the yaxis, I answer your question based on this random data:
import pandas as pd
import numpy as np
s = pd.Series(pd.timedelta_range(start='1 days', end='75 days'))
df = pd.DataFrame()
df['commits'] = np.random.randint(100, size=len(s))
df['Version'] = 'v'+df['commits'].astype(str)
df['Age (in days)'] = s
fig = px.scatter(df, x="Version", y=df['Age (in days)'].dt.days, color="commits", width =1000, height = 800)
fig.update_yaxes(ticksuffix = " days")
fig.show()
Output
I have this dataset:
df = pd.DataFrame()
df['year'] = [2011,2011,2011,2011,2011,2011,2011,2011,2011,2011,2011,2011]
df['month'] = [1,2,3,4,5,6,1,2,3,4,5,6]
df['after'] = [0,0,0,1,1,1,0,0,0,1,1,1]
df['campaign'] = [0,0,0,0,0,0,1,1,1,1,1,1]
df['sales'] = [10000,11000,12000,10500,10000,9500,7000,8000,5000,6000,6000,7000]
df['date_m'] = pd.to_datetime(df.year.astype(str) + '-' + df.month.astype(str))
And I want to make a line plot grouped by month and campaign, so I have tried this code:
df['sales'].groupby(df['date_m','campaign']).mean().plot.line()
But I get this error message KeyError: ('date_m', 'campaign'). Please, any help will be greatly appreciated.
Plotting is typically dependant upon the shape of the DataFrame.
.groupby creates a long format DataFrame, which is great for seaborn
.pivot_table creates a wide format DataFrame, which easily works with pandas.DataFrame.plot
.groupby the DataFrame
df['sales'].groupby(...) is incorrect, because df['sales'] selects one column of the dataframe; none of the other columns are available
.groupby converts the DataFrame into a long format, which is great for plotting with seaborn.lineplot.
Specify the hue parameter to separate by 'campaign'.
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# perform groupby and reset the index
dfg = df.groupby(['date_m','campaign'])['sales'].mean().reset_index()
# display(dfg.head())
date_m campaign sales
0 2011-01-01 0 10000
1 2011-01-01 1 7000
2 2011-02-01 0 11000
3 2011-02-01 1 8000
4 2011-03-01 0 12000
# plot with seaborn
sns.lineplot(data=dfg, x='date_m', y='sales', hue='campaign')
.pivot_table the DataFrame
.pivot_table shapes the DataFrame correctly for plotting with pandas.DataFrame.plot, and it has an aggregation parameter.
The DataFrame is shaped into a wide format.
# pivot the dataframe into the correct shape for plotting
dfp = df.pivot_table(index='date_m', columns='campaign', values='sales', aggfunc='mean')
# display(dfp.head())
campaign 0 1
date_m
2011-01-01 10000 7000
2011-02-01 11000 8000
2011-03-01 12000 5000
2011-04-01 10500 6000
2011-05-01 10000 6000
# plot the dataframe
dfp.plot()
Plotting with matplotlib directly
fig, ax = plt.subplots(figsize=(8, 6))
for v in df.campaign.unique():
# select the data based on the campaign
data = df[df.campaign.eq(v)]
# this is only necessary if there is more than one value per date
data = data.groupby(['date_m','campaign'])['sales'].mean().reset_index()
ax.plot('date_m', 'sales', data=data, label=f'{v}')
plt.legend(title='campaign')
plt.show()
Notes
Package versions:
pandas v1.2.4
seaborn v0.11.1
matplotlib v3.3.4
I have created a visualization utilizing the plotly library within Python. Everything looks fine, except the axis is starting with 2020 and then shows 2019. The axis should be the opposite of what is displayed.
Here is the data (df):
date percent type
3/1/2020 10 a
3/1/2020 0 b
4/1/2020 15 a
4/1/2020 60 b
1/1/2019 25 a
1/1/2019 1 b
2/1/2019 50 c
2/1/2019 20 d
This is what I am doing
import plotly.express as px
px.scatter(df, x = "date", y = "percent", color = "type", facet_col = "type")
How would I make it so that the dates are sorted correctly, earliest to latest? The dates are sorted within the raw data so why is it not reflecting this on the graph?
Any suggestion will be appreciated.
Here is the result:
It is plotting in the order of your df. If you want date order then sort so in date order.
df.sort_values('date', inplace=True)
A lot of other graphing utilities (Seaborn, etc) by default sort when plotting. Plotly Express does not do this.
Your date column seems to be a string. If you convert it to a datetime you don't have to sort your dataframe: plotly express will set the x-axis to datetime:
Working code example:
import pandas as pd
import plotly.express as px
from io import StringIO
text = """
date percent type
3/1/2020 10 a
3/1/2020 0 b
4/1/2020 15 a
4/1/2020 60 b
1/1/2019 25 a
1/1/2019 1 b
2/1/2019 50 c
2/1/2019 20 d
"""
df = pd.read_csv(StringIO(text), sep='\s+', header=0)
px.scatter(df, x="date", y="percent", color="type", facet_col="type")
import plotly.offline as pyo
import plotly.express as px
import matplotlib.pyplot as pls
pyo.init_notebook_mode()
data = pd.read_csv(r'C:.......Coronovirus Datasets\time_series_covid19_deaths_global.csv')
countries = ['US']
filtered_data = data[data['Country/Region'].isin(countries)]
wanted_values = filtered_data[['Country/Region','1/22/2020','1/23/2020','1/24/2020', '1/25/2020','1/26/2020','1/27/2020','1/28/2020','1/28/2020','1/29/2020',
'1/30/2020','1/31/2020','2/1/2020','2/2/2020','2/3/2020','2/4/2020','2/5/2020','2/6/2020','2/7/2020','2/8/2020','2/9/2020','2/10/2020',
'2/11/2020','2/12/2020','2/13/2020','2/14/2020','2/15/2020','2/16/2020','2/17/2020','2/18/2020','2/19/2020','2/20/2020','2/21/2020','2/22/2020','2/23/2020',
'2/24/2020','2/25/2020','2/26/2020','2/27/2020','2/28/2020','2/29/2020','3/1/2020','3/2/2020','3/3/2020','3/4/2020','3/5/2020','3/6/2020','3/7/2020',
'3/8/2020','3/9/2020','3/10/2020','3/11/2020','3/12/2020','3/13/2020','3/14/2020','3/15/2020','3/16/2020','3/17/2020','3/18/2020','3/19/2020',
'3/20/2020','3/21/2020','4/1/2020','4/2/2020','4/3/2020','4/4/2020','4/5/2020','4/6/2020','4/7/2020','4/8/2020','4/9/2020','4/10/2020',
'4/11/2020','4/12/2020','4/13/2020','4/14/2020','4/15/2020','4/16/2020','4/17/2020','4/18/2020','4/19/2020','4/20/2020','4/21/2020','4/22/2020','4/23/2020',
'4/24/2020','4/25/2020','4/26/2020','4/27/2020','4/28/2020','4/29/2020','5/1/2020','5/2/2020','5/3/2020','5/4/2020','5/5/2020','5/6/2020','5/7/2020','5/8/2020','5/9/2020']]
fig = px.scatter(wanted_values, x ='Country/Region', y = 'dates' , title = 'Number of Deaths Per Day')
fig.show()
#wanted_values.plot(x="5/9/2020, 5/8/2020", y = 'filtered_data' kind = 'bar')
#pls.show()
How can I plot all the dates with their corresponding deaths as a scatter plot? I plan to use linear regression to predict the amount of deaths since January first. I have been having a lot of trouble with plotting these values as I am really new to Python.
The data set can be found here: https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
This is how your data looks like:
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv("time_series_covid19_deaths_global.csv")
data.iloc[:2,:7]
Province/State Country/Region Lat Long 1/22/20 1/23/20 1/24/20
0 NaN Afghanistan 33.0000 65.0000 0 0 0
1 NaN Albania 41.1533 20.1683 0 0 0
First of all, subset it by giving it the start and end of dates (that match the column names) and melting it to give long format:
data = data[data['Country/Region']=='US']
data = data.loc[:,'1/22/20':'5/9/20'].melt(var_name="date")
data['date'] = pd.to_datetime(data['date'])
Looks like this now:
date value
0 2020-01-22 0
1 2020-01-23 0
2 2020-01-24 0
Plotting is simply:
data.plot.scatter(x="date",y="value",rot=45)
My Goal is just to plot this simple data, as a graph, with x data being dates ( date showing in x-axis) and price as the y-axis. Understanding that the dtype of the NumPy record array for the field date is datetime64[D] which means it is a 64-bit np.datetime64 in 'day' units. While this format is more portable, Matplotlib cannot plot this format natively yet. We can plot this data by changing the dates to DateTime.date instances instead, which can be achieved by converting to an object array: which I did below view the astype('0'). But I am still getting
this error :
view limit minimum -36838.00750000001 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-DateTime value to an axis that has DateTime units
code:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv(r'avocado.csv')
df2 = df[['Date','AveragePrice','region']]
df2 = (df2.loc[df2['region'] == 'Albany'])
df2['Date'] = pd.to_datetime(df2['Date'])
df2['Date'] = df2.Date.astype('O')
plt.style.use('ggplot')
ax = df2[['Date','AveragePrice']].plot(kind='line', title ="Price Change",figsize=(15,10),legend=True, fontsize=12)
ax.set_xlabel("Period",fontsize=12)
ax.set_ylabel("Price",fontsize=12)
plt.show()
df.head(3)
Unnamed: 0 Date AveragePrice Total Volume 4046 4225 4770 Total Bags Small Bags Large Bags XLarge Bags type year region
0 0 2015-12-27 1.33 64236.62 1036.74 54454.85 48.16 8696.87 8603.62 93.25 0.0 conventional 2015 Albany
1 1 2015-12-20 1.35 54876.98 674.28 44638.81 58.33 9505.56 9408.07 97.49 0.0 conventional 2015 Albany
2 2 2015-12-13 0.93 118220.22 794.70 109149.67 130.50 8145.35 8042.21 103.14 0.0 conventional 2015 Albany
df2 = df[['Date', 'AveragePrice', 'region']]
df2 = (df2.loc[df2['region'] == 'Albany'])
df2['Date'] = pd.to_datetime(df2['Date'])
df2 = df2[['Date', 'AveragePrice']]
df2 = df2.sort_values(['Date'])
df2 = df2.set_index('Date')
print(df2)
ax = df2.plot(kind='line', title="Price Change")
ax.set_xlabel("Period", fontsize=12)
ax.set_ylabel("Price", fontsize=12)
plt.show()
output: