Reputation: 507
I want to make multiple Plotly Scatter plots (one for each column) in a df using a for loop in Python, which I get to work as expected.
Then I want the color of the plots to be conditional of the last value in the dataframe. Eg. if the last row is above or below 5, so in the example I want the plot for column B to have a differrent color than A and C.
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'A': [3, 1, 2, 3],
'B': [5, 6, 7, 8],
'C': [2, 1, 6, 3]})
df
A B C
0 3 5 2
1 1 6 1
2 2 7 6
3 3 8 3
making plots with Plotly and for loop:
plots = {}
for i in df.columns:
if df.iloc[-1] >= 5:
plots[i] = px.scatter(df, color_discrete_sequence=["red"], x="A", y=i)
else:
plots[i] = px.scatter(df, color_discrete_sequence=["blue"], x="A", y=i)
Here I expect to be able to show each plot with plots['A'], plots['B'] and plots['C'], but I expect plot A and C to be blue, and plot B to be red.
Upvotes: 1
Views: 1369
Reputation: 61104
Structurally, your approach seems to be fine. But there are a few details that may cause some problems. df.iloc[-1] >= 5
can quickly raise an error, so I'd use dfx.iloc[-1].values >= 5
instead. Your dataframe subsetting isn't working either. And when you're subsetting a dataframe column by column, you often get a pandas series rather than a pandas dataframe. And their attributes are not the same. Taking these details intor consideration, I've put together the code below that produces the following plot by calling, for example, plots[2].show()
:
Give it a try an let me know how it works out for you.
import plotly.express as px
import pandas as pd
df = pd.DataFrame({'A': [3, 1, 2, 3],
'B': [5, 6, 7, 8],
'C': [2, 1, 6, 3]})
plots = {}
for i, col in enumerate(df.columns):
dfx = df[col].to_frame()
print(dfx)
if dfx.iloc[-1].values >= 5:
plots[i] = px.scatter(df, color_discrete_sequence=["red"], x=df[col].index, y=df[col])
else:
plots[i] = px.scatter(df, color_discrete_sequence=["blue"], x=df[col].index, y=df[col])
plots[2].show()
Upvotes: 1