python - ValueError: view limit minimum -35738.3640567 is less than 1 and is an invalid Matplotlib date value

Question

im a beginner in matplotlib. Im trying to plot a dataframe using matplotlib.pyplot. The problem is that everytime I try to plot it i get the following error:

ValueError: view limit minimum -35738.3640567 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.

According to the error, it seems to be like theres a non-datetime value in the datetime column, but there isnt.

Ive tried using pd.to_datetime() and try to change the format of the timestamp to pd.to_datetime(df_google['datetime'], format = '%d/%m/%Y') but nothing changes.

This is the code im trying to use:

import matplotlib.pyplot as plt

df_google.plot()
plt.show()

df_google is a dataframe with columns ['datetime','price'] and some of the values are the following:

     datetime        price
0  2018-05-15  1079.229980
1  2018-05-16  1081.770020
2  2018-05-17  1078.589966
3  2018-05-18  1066.359985
4  2018-05-21  1079.579956
5  2018-05-22  1069.729980
6  2018-05-23  1079.689941
7  2018-05-24  1079.239990
8  2018-05-25  1075.660034
9  2018-05-29  1060.319946

Can someone try to help me understand this type of error? Why does it says theres a non-datetime value when every value is a datetime type value? How can I plot this dataframe?

Answer 1

Set the 'datetime' column to a datetime64[ns] type:

• Use pandas.to_datetime to convert the 'datetime' column, and remember to assign the column back to itself, because this is not an inplace update.
• Column names can be accessed with a ., if they do not contain special characters and do not clash with built-in attributes/methods (e.g., index, count).
  • df_google.datetime instead of df_google['datetime']

import pandas as pd
import matplotlib.pyplot as plt

# given the following data
data = {'datetime': ['2018-05-15', '2018-05-16', '2018-05-17', '2018-05-18', '2018-05-21', '2018-05-22', '2018-05-23', '2018-05-24', '2018-05-25', '2018-05-29'],
        'price': [1079.22998, 1081.77002, 1078.589966, 1066.359985, 1079.579956, 1069.72998, 1079.689941, 1079.23999, 1075.660034, 1060.319946]}

df_google = pd.DataFrame(data)

# convert the datetime column to a datetime type and assign it back to the column
df_google.datetime = pd.to_datetime(df_google.datetime)

# display(df_google.head())
     datetime        price
0  2018-05-15  1079.229980
1  2018-05-16  1081.770020
2  2018-05-17  1078.589966
3  2018-05-18  1066.359985
4  2018-05-21  1079.579956
5  2018-05-22  1069.729980
6  2018-05-23  1079.689941
7  2018-05-24  1079.239990
8  2018-05-25  1075.660034
9  2018-05-29  1060.319946

Verify the 'datetime' column is a datetime64[ns] Dtype:

print(df_google.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype         
---  ------    --------------  -----         
 0   datetime  10 non-null     datetime64[ns]
 1   price     10 non-null     float64       
dtypes: datetime64[ns](1), float64(1)
memory usage: 288.0 bytes

Plot:

df_google.plot(x='datetime')
plt.show()

• There's a substantial ecosystem of alternative plotting tools, but df.plot() is fine for getting a look at the data.
  • PyViz