问题
I have a dataframe that records concentrations for several different locations in different years, with a high temporal frequency (<1 hour). I am trying to make a bar/multibar plot showing mean concentrations, at different locations in different years
To calculate mean concentration, I have to apply quality control filters to daily and monthly data.
My approach is to first apply filters and resample per year and then do the grouping by location and year.
Also, out of all the locations (in the column titled locations) I have to choose only a few rows. So, I am slicing the original dataframe and creating a new dataframe with selected rows.
I am not able to achieve this using the following code:
date=df['date']
location = df['location']
df.date = pd.to_datetime(df.date)
year=df.date.dt.year
df=df.set_index(date)
df['Year'] = df['date'].map(lambda x: x.year )
#Location name selection/correction in each city:
#Changing all stations:
df['location'] = df['location'].map(lambda x: "M" if x == "mm" else x)
#New dataframe:
df_new = df[(df['location'].isin(['K', 'L', 'M']))]
#Data filtering:
df_new = df_new[df_new['value'] >= 0]
df_new.drop(df_new[df_new['value'] > 400].index, inplace = True)
df_new.drop(df_new[df_new['value'] <2].index, inplace = True)
diurnal = df_new[df_new['value']].resample('12h')
diurnal_mean = diurnal.mean()[diurnal.count() >= 9]
daily_mean=diurnal_mean.resample('d').mean()
df_month=daily_mean.resample('m').mean()
df_yearly=df_month[df_month['value']].resample('y')
#For plotting:
df_grouped = df_new.groupby(['location', 'Year']).agg({'value':'sum'}).reset_index()
sns.barplot(x='location',y='value',hue='Year',data= df_grouped)
This is one of the many errors that cropped up:
"None of [Float64Index([22.73, 64.81, 8.67, 19.98, 33.12, 37.81, 39.87, 42.29, 37.81,\n 36.51,\n ...\n 11.0, 40.0, 23.0, 80.0, 50.0, 60.0, 40.0, 80.0, 80.0,\n 17.0],\n dtype='float64', length=63846)] are in the [columns]"
ERROR:root:Invalid alias: The name clear can't be aliased because it is another magic command.
This is a sample dataframe, showing what I need to plot; value column should ideally represent resampled values, after performing the quality control operations and resampling.
Unnamed: 0 location value \
date location value
2017-10-21 08:45:00+05:30 8335 M 339.3
2017-08-18 17:45:00+05:30 8344 M 45.1
2017-11-08 13:15:00+05:30 8347 L 594.4
2017-10-21 13:15:00+05:30 8659 N 189.9
2017-08-18 15:45:00+05:30 8662 N 46.5
This is how the a part of the actual data should look like, after selecting the chosen locations. I am a new user so cannot attach a screenshot of the graph I require. This query is an extension of the query I had posted earlier , with the additional requirement of plotting resampled data instead of simple value counts. Iteration over years to plot different group values as bar plot in pandas
Any help will be much appreciated.
回答1:
Fundamentally, your errors come with this unclear indexing where you are passing continuous, float values of one column for rowwise selection of index which currently is a datetime type.
df_new[df_new['value']] # INDEXING DATETIME USING FLOAT VALUES
...
df_month[df_month['value']] # COLUMN value DOES NOT EXIST
Possibly, you meant to select the column value (out of the others) during resampling.
diurnal = df_new['value'].resample('12h')
diurnal.mean()[diurnal.count() >= 9]
daily_mean = diurnal_mean.resample('d').mean()
df_month = daily_mean.resample('m').mean() # REMOVE value BEING UNDERLYING SERIES
df_yearly = df_month.resample('y')
However, no where above do you retain location for plotting. Hence, instead of resample, use groupby(pd.Grouper(...))
# AGGREGATE TO KEEP LOCATION AND 12h
diurnal = (df_new.groupby(["location", pd.Grouper(freq='12h')])["value"]
.agg(["count", "mean"])
.reset_index().set_index(['date'])
)
# FILTER
diurnal_sub = diurnal[diurnal["count"] >= 9]
# MULTIPLE DATE TIME LEVEL MEANS
daily_mean = diurnal_sub.groupby(["location", pd.Grouper(freq='d')])["mean"].mean()
df_month = diurnal_sub.groupby(["location", pd.Grouper(freq='m')])["mean"].mean()
df_yearly = diurnal_sub.groupby(["location", pd.Grouper(freq='y')])["mean"].mean()
print(df_yearly)
To demonstrate with random, reproducible data:
Data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(242020)
random_df = pd.DataFrame({'date': (np.random.choice(pd.date_range('2017-01-01', '2019-12-31'), 5000) +
pd.to_timedelta(np.random.randint(60*60, 60*60*24, 5000), unit='s')),
'location': np.random.choice(list("KLM"), 5000),
'value': np.random.uniform(10, 1000, 5000)
})
Aggregation
loc_list = list("KLM")
# NEW DATA FRAME WITH DATA FILTERING
df = (random_df.set_index(random_df['date'])
.assign(Year = lambda x: x['date'].dt.year,
location = lambda x: x['location'].where(x["location"] != "mm", "M"))
.query('(location == @loc_list) and (value >= 2 and value <= 400)')
)
# 12h AGGREGATION
diurnal = (df_new.groupby(["location", pd.Grouper(freq='12h')])["value"]
.agg(["count", "mean"])
.reset_index().set_index(['date'])
.query("count >= 2")
)
# d, m, y AGGREGATION
daily_mean = diurnal.groupby(["location", pd.Grouper(freq='d')])["mean"].mean()
df_month = diurnal.groupby(["location", pd.Grouper(freq='m')])["mean"].mean()
df_yearly = (diurnal.groupby(["location", pd.Grouper(freq='y')])["mean"].mean()
.reset_index()
.assign(Year = lambda x: x["date"].dt.year)
)
print(df_yearly)
# location date mean Year
# 0 K 2017-12-31 188.984592 2017
# 1 K 2018-12-31 199.521702 2018
# 2 K 2019-12-31 216.497268 2019
# 3 L 2017-12-31 214.347873 2017
# 4 L 2018-12-31 199.232711 2018
# 5 L 2019-12-31 177.689221 2019
# 6 M 2017-12-31 222.412711 2017
# 7 M 2018-12-31 241.597977 2018
# 8 M 2019-12-31 215.554228 2019
Plotting
sns.set()
fig, axs = plt.subplots(figsize=(12,5))
sns.barplot(x='location', y='mean', hue='Year', data= df_yearly, ax=axs)
plt.title("Location Value Yearly Aggregation", weight="bold", size=16)
plt.show()
plt.clf()
plt.close()
来源：https://stackoverflow.com/questions/60047258/pandas-grouping-and-resampling-for-a-bar-plot