I have the following datasets:
df1 = {'lower':[3.99,4.99,5.99,1700], 'percentile':[1,2,5,10,50,100]}
df2 = {'lower':[2.99,4.50,5,1850], 'percentile':[2,4,7,15,55,100]}
The data:
The percentile refers to the percentage of the data that corresponds
to a particular price e.g: 3.99 would represent 1% of the data while
all values under 5.99 would represent 5% of the data.
The length of the two datasets is 100 given that we are showing percentiles, but they vary between the two datasets as the price.
What I have done so far:
What I need help with:
As you see in the third graph, I can plot the two datasets overlayed, which is what I need, but I have been unsuccessful trying to change the legend and the weird tick x values on the third graph. It is not showing the percentile, or other metrics I might use the x axis with.
Any help?
Related
I have been having Problems with price column every time I try to plot graphs on it and all my graphs have this problem and I want to change it to its actual values instead of decimals
Example of of linear graph
This is the dataframe containing the information of the dataset
Train is the name of dataframe.
Column contains the selected
columns = ['Id', 'year', 'distance_travelled(kms)', 'brand_rank', 'car_age']
for i in columns:
plt.scatter(train[i], y, label='Actual')
plt.xlabel(i)
plt.ylabel('price')
plt.show()
I am trying to make a line chart with two metrics vs date. I am querying an already aggregated table. When I add three columns to the visualization, date and 2 numeric fields and pick scatter plot or line chart, only two numeric fields are charted. I want to be able to chart date as X axis and two numeric fields as line chart.
Is there a way to chart two numeric dimensions in one chart without aggregation?
You need to have one dimension, which is the date, and two measures with type:number (because you don't need any aggregation)
measure: total_amount {
type: number
sql: ${pre_aggregated_total_amount} ;;
measure: product_count {
type: number
sql: ${pre_aggregated_product_count} ;;
Also, in the line chart option, you will be able to drag one measure to the left axis and the other one to the right axis if you want.
I am trying to make a plotly line chart that shows team member progression with the following excel data:
For the life of me I cannot figure out how to set the team member names as the color for the lines, the x-axis as the months, and the y-axis as the numeric values. Closest I've gotten is a blank graph, and I've tried about 400 combinations of parameters for
px.line(
df,
color="Team_Member",
x = "the row of months... something with iloc maybe?",
y = df.columns,
title="Average Estimated Daily Working Hours by Team Member"
)
I'm new to data science & pandas. I'm just trying to visualize the distribution of data from a single series (a single column), but the histogram that I'm generating is only a single column (see below where it's sorted descending).
My data is over 11 million rows. The max value is 27,235 and the min values are 1. I'd like to see the "count" column grouped into different bins and a column/bar whose height is the total for each bin. But, I'm only seeing a single bar and am not sure what to do.
Data
df = pd.DataFrame({'count':[27235,26000,25877]})
Solution
import matplotlib.pyplot as plt
df['count'].hist()
Alternatively
sns.distplot(df['count'])
I want to plot two data series in one plot, but when plotting both data series, one of the series are changing. Matplotlib draws lines between the wrong data.
Firsty_values and secondy_values are lists of timestamps sorted and stretching one 24h interval.
Firstx_values and secondx_values are values in the range 18-21.
The first plot shows the two series together while the last plot shows one of the series alone.
#Firsty_values and secondy_values looks like this:
#['2019-05-04 00:00:03',
# '2019-05-04 00:02:03',
# ...
# '2019-05-04 23:56:03',
# '2019-05-04 23:58:02']
#Firstx_values and secondx_values looks like this:
#[18.32,18.34 ..... 19.32,19.31]
plt.plot(firsty_values,firstx_values,'b')
plt.plot(secondy_values, secondx_values, 'g')
plt.ylabel('Temperature [C]')
plt.xlabel('Time')
plt.legend(['SA1_563_04_RT601A', 'SA1_563_04_RT601B'])
plt.xticks([100,604,1053]) #length more than 1053
plt.show()
#plt.plot(firsty_values,firstx_values,'b')
plt.plot(secondy_values, secondx_values, 'g')
plt.ylabel('Temperature [C]')
plt.xlabel('Time')
plt.legend(['SA1_563_04_RT601A', 'SA1_563_04_RT601B'])
plt.xticks([100,604,1053]) #length less than 1053
plt.show()
Output:
Output with both data series :
Output with one data series :
First plot draw lines between data points that does not lie next to each other. The problem seems to be that some of the data points from the second series are put out of order after the points from the first series. This is reflected by the "xticks" showing three lables when ploting both and two lables when ploting one series.