CODEWITHSUNDEEP
pythonpandasnumpydataframematplotlib
nilsinelabore
nilsinelabore

Reputation: 5115

Mark specific points based on conditions in Matplotlib

I plotted min points for df['Data']. 

Timestamp = pd.date_range('2020-02-06 08:23:04', periods=1000, freq='s')
df = pd.DataFrame({'Timestamp': Timestamp,
                   'Data': 30+15*np.cos(np.linspace(0,10,Timestamp.size))})

df['timediff'] = (df['Timestamp'].shift(-1) - df['Timestamp']).dt.total_seconds()   
df['datadiff'] = df['Data'].shift(-1) - df['Data']
df['gradient'] = df['datadiff'] / df['timediff']

min_pt = np.min(df['Data'])       
# filter_pt = df.loc(df['gradient'] >= -0.1) # & df.loc[i, 'gradient'] <=0.1

mask = np.array(df['Data']) == min_pt 
color = np.where(mask, 'blue', 'yellow')

fig,ax = plt.subplots(figsize=(20,10))
# plt.plot_date(df['Timestamp'], df['Data'], '-' )
ax.scatter(df['Timestamp'], df['Data'], color=color, s=10)
plt.ticklabel_format
plt.show()

The plot looks like this: enter image description here

I want to extend the condition using df['gradient'] column:

  1. What if instead of marking only 'minimum' points, I want to mark the points where gradient lies between 0.1 and -0.1 inclusive?
  2. Additional condition: Take only the first datapoint in such range(ie.0.1 and -0.1 inclusive).
  3. How to loop through whole dataset, rather than just taking the first data point that satisfies these conditions(what my current plot did)?

Tried to add:


df1 = df[df.gradient <= 0.1 & df.gradient >= -0.1]
plt.plot(df1.Timestamp,df1.Data, label="filter")

before mask based on this answer which returned error:

TypeError: Cannot perform 'rand_' with a dtyped [float64] array and scalar of type [bool]

I think what I did wasn't very efficient. How to do it more efficiently?

Update:

With code

Timestamp = pd.date_range('2020-02-06 08:23:04', periods=1000, freq='s')
df = pd.DataFrame({'Timestamp': Timestamp,
                   'Data': 30+15*np.cos(np.linspace(0,10,Timestamp.size))})

df['timediff'] = (df['Timestamp'].shift(-1) - df['Timestamp']).dt.total_seconds()    
df['datadiff'] = df['Data'].shift(-1) - df['Data']
df['gradient'] = df['datadiff'] / df['timediff']

fig,ax = plt.subplots(figsize=(20,10))
df1 = df[(df.gradient <= 0.1) & (df.gradient >= -0.1)]
plt.plot(df1.Timestamp,df1.Data, label="filter")
plt.show()

it returned enter image description here

After changing the range to 

df1 = df[(df.gradient <= 0.01) & (df.gradient >= -0.01)]

it returned enter image description here

Why?

Upvotes: 0

Views: 413

Answers (1)

jcaliz
jcaliz

Reputation: 4021

Add the parenthesis on each condition that way you can do logical and row by row

df1 = df[(df.gradient <= 0.1) & (df.gradient >= -0.1)]

And consider using some scatter, otherwise, the latest points where the absolute value of gradient is greater than 0.1 will be connected.

plt.scatter(df1.Timestamp,df1.Data, label="filter")

This would be the final image:

enter image description here

EDIT

If you need only the first point where gradient is in the range, create groups and then use groupby

df['groups'] = ((df.gradient > 0.1) | (df.gradient < -0.1)).cumsum()

df2 = df[(df.gradient <= 0.1) & (df.gradient >= -0.1)]
    .groupby('groups').agg({'Timestamp':'first', 'Data':'first'})

#        Timestamp              Data
# groups        
# 0      2020-02-06 08:23:04    45.000000
# 168    2020-02-06 08:27:05    18.814188
# 336    2020-02-06 08:32:19    41.201294
# 504    2020-02-06 08:37:33    18.783251

Upvotes: 1

Related Questions