Why isn't this code to plot a histogram on a continuous value Pandas column working?
Solution 1
EDIT:
After your comments this actually makes perfect sense why you don't get a histogram of each different value. There are 1.4 million rows, and ten discrete buckets. So apparently each bucket is exactly 10% (to within what you can see in the plot).
A quick rerun of your data:
In [25]: df.hist(column='Trip_distance')
Prints out absolutely fine.
The df.hist
function comes with an optional keyword argument bins=10
which buckets the data into discrete bins. With only 10 discrete bins and a more or less homogeneous distribution of hundreds of thousands of rows, you might not be able to see the difference in the ten different bins in your low resolution plot:
In [34]: df.hist(column='Trip_distance', bins=50)
Solution 2
Here's another way to plot the data, involves turning the date_time into an index, this might help you for future slicing
#convert column to datetime
trip_data['lpep_pickup_datetime'] = pd.to_datetime(trip_data['lpep_pickup_datetime'])
#turn the datetime to an index
trip_data.index = trip_data['lpep_pickup_datetime']
#Plot
trip_data['Trip_distance'].plot(kind='hist')
plt.show()
Baktaawar
Updated on May 26, 2020Comments
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Baktaawar about 4 years
I am trying to create a histogram on a continuous value column
Trip_distance
in a large 1.4M row pandas dataframe. Wrote the following code:fig = plt.figure(figsize=(17,10)) trip_data.hist(column="Trip_distance") plt.xlabel("Trip_distance",fontsize=15) plt.ylabel("Frequency",fontsize=15) plt.xlim([0.0,100.0]) #plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
But I am not sure why all values give the same frequency plot which shouldn't be the case. What's wrong with the code?
Test data:
VendorID lpep_pickup_datetime Lpep_dropoff_datetime Store_and_fwd_flag RateCodeID Pickup_longitude Pickup_latitude Dropoff_longitude Dropoff_latitude Passenger_count Trip_distance Fare_amount Extra MTA_tax Tip_amount Tolls_amount Ehail_fee improvement_surcharge Total_amount Payment_type Trip_type 0 2 2015-09-01 00:02:34 2015-09-01 00:02:38 N 5 -73.979485 40.684956 -73.979431 40.685020 1 0.00 7.8 0.0 0.0 1.95 0.0 NaN 0.0 9.75 1 2.0 1 2 2015-09-01 00:04:20 2015-09-01 00:04:24 N 5 -74.010796 40.912216 -74.010780 40.912212 1 0.00 45.0 0.0 0.0 0.00 0.0 NaN 0.0 45.00 1 2.0 2 2 2015-09-01 00:01:50 2015-09-01 00:04:24 N 1 -73.921410 40.766708 -73.914413 40.764687 1 0.59 4.0 0.5 0.5 0.50 0.0 NaN 0.3 5.80 1 1.0 3 2 2015-09-01 00:02:36 2015-09-01 00:06:42 N 1 -73.921387 40.766678 -73.931427 40.771584 1 0.74 5.0 0.5 0.5 0.00 0.0 NaN 0.3 6.30 2 1.0 4 2 2015-09-01 00:00:14 2015-09-01 00:04:20 N 1 -73.955482 40.714046 -73.944412 40.714729 1 0.61 5.0 0.5 0.5 0.00 0.0 NaN 0.3 6.30 2 1.0 5 2 2015-09-01 00:00:39 2015-09-01 00:05:20 N 1 -73.945297 40.808186 -73.937668 40.821198 1 1.07 5.5 0.5 0.5 1.36 0.0 NaN 0.3 8.16 1 1.0 6 2 2015-09-01 00:00:52 2015-09-01 00:05:50 N 1 -73.890877 40.746426 -73.876923 40.756306 1 1.43 6.5 0.5 0.5 0.00 0.0 NaN 0.3 7.80 1 1.0 7 2 2015-09-01 00:02:15 2015-09-01 00:05:34 N 1 -73.946701 40.797321 -73.937645 40.804516 1 0.90 5.0 0.5 0.5 0.00 0.0 NaN 0.3 6.30 2 1.0 8 2 2015-09-01 00:02:36 2015-09-01 00:07:20 N 1 -73.963150 40.693829 -73.956787 40.680531 1 1.33 6.0 0.5 0.5 1.46 0.0 NaN 0.3 8.76 1 1.0 9 2 2015-09-01 00:02:13 2015-09-01 00:07:23 N 1 -73.896820 40.746128 -73.888626 40.752724 1 0.84 5.5 0.5 0.5 0.00 0.0 NaN 0.3 6.80 2 1.0 In [ ]:
Trip_distance column 0 0.00 1 0.00 2 0.59 3 0.74 4 0.61 5 1.07 6 1.43 7 0.90 8 1.33 9 0.84 10 0.80 11 0.70 12 1.01 13 0.39 14 0.56 Name: Trip_distance, dtype: float64
After 100 bins: