Error: Change the color of a bar if condition is True matplotlib

Question

I have a pandas DataFrame with positive and negative values as a bar chart. How can I plot "orange" for positive values ​​and "sky blue" for negative values?

The code when the color is not changed is:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
%matplotlib inline

# create some fake data
Value = pd.Series([-20, -15, 18, -8, 6, 7, 10, 2, 10, 4], 
                        index=['Rent', 'Transportation', 'Bills', 'Food', 
                               'Travel', 'Entertainment', 'Health', 'Other', 'Clothes', 'Phone'])
df = pd.DataFrame({'Value' : Value})
df = df.sort_values(by='Value')

my_range=list(range(1,len(df.index)+1))
fig, ax = plt.subplots(figsize=(5,3.5))

#color
clrs='orange'

# create for each expense type an horizontal line that starts at x = 0 with the length 
# represented by the specific expense percentage value.
plt.hlines(y=my_range, xmin=0, xmax=df['Value'], color=clrs, alpha=0.2, linewidth=5)

# create for each expense type a dot at the level of the expense percentage value
plt.plot(df['Value'], my_range, "o", markersize=5, color=clrs, alpha=0.6)

I tried to change the color:

clrs = np.where(df['percentage']>0, 'orange', 'skyblue')

However I got:

ValueError: Invalid RGBA argument: array(['skyblue', 'skyblue', 'skyblue', 'orange', 'orange', 'orange',
       'orange', 'orange', 'orange', 'orange'], dtype='<U7')

I have checked posts related to Invalid RGBA argument(This, This) and how to change the color(This, This), but they don't work.
Can anyone help?

Answer 1

You can split your dataframe into positive and negative values and plot them separately:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
%matplotlib inline


# create some fake data
Value = pd.Series([-20, -15, 18, -8, 6, 7, 10, 2, 10, 4], 
                    index=['Rent', 'Transportation', 'Bills', 'Food', 
                           'Travel', 'Entertainment', 'Health', 'Other', 'Clothes', 'Phone'])
df = pd.DataFrame({'Value' : Value})
df = df.sort_values(by='Value')

# Separate the dataframe into positive and negative values
df_pos = df[(df>0)]
df_neg= df[(df<0)]

my_range_pos=list(range(1,len(df_pos.index)+1))
my_range_neg=list(range(1,len(df_neg.index)+1))

fig, ax = plt.subplots(figsize=(5,3.5))

#color
clrs='orange'

# create for each expense type an horizontal line that starts at x = 0 with the length 
# represented by the specific expense percentage value.
# Plot positive
plt.hlines(y=my_range_pos, xmin=0, xmax=df_pos['Value'], color='orange', alpha=0.2, linewidth=5)
# Plot negative
plt.hlines(y=my_range_neg, xmin=0, xmax=df_neg['Value'], color='blue', alpha=0.2, linewidth=5)

# create for each expense type a dot at the level of the expense percentage value
# Plot positive
plt.plot(df_pos['Value'], my_range_pos, "o", markersize=5, color='orange', alpha=0.6)
# Plot negative
plt.plot(df_neg['Value'], my_range_neg, "o", markersize=5, color='blue', alpha=0.6)

Result: