
In [3]:
import pandas as pd
import seaborn as sns
df2 = pd.read_csv('c:/8/dots2.txt')
df2.sample(8)
Out[3]:
In [4]:
sns.set(style="dark")
In [5]:
df2['align'].value_counts()
Out[5]:
In [6]:
df2['choice'].value_counts()
Out[6]:
In [7]:
sns.relplot(x="time", y="firing_rate",hue="coherence", size="choice", col="align", kind="line", data=df2)
Out[7]:
In [8]:
df2['coherence'].value_counts()
Out[8]:
Odwrócenie osi
In [9]:
sns.relplot(x="time", y="firing_rate",hue="coherence", size="align", col="choice", kind="line", data=df2)
Out[9]:
In [10]:
sns.set(style="whitegrid")
sns.relplot(x="time", y="firing_rate",hue="coherence", size="choice", col="align",height=6, aspect=.65,legend="full", palette="coolwarm", kind="line",data=df2)
Out[10]:
Zanieczyszczenie chińskich miast
In [11]:
import pandas as pd
import seaborn as sns
import matplotlib as plt
df = pd.read_csv('c:/8/ShenyangPM20100101_20151231.csv')
df.head(3)
Out[11]:
In [12]:
df['cbwd'].value_counts()
Out[12]:
In [14]:
df['winter/summer']= df['month'].apply(lambda x: " winter" if x < 4 or x > 10 else " summer ")
In [15]:
kot = ['pressure High','pressure Medium','pressure Low']
df['Pres_cat'] = pd.qcut(df['PRES'],3, labels=kot)
In [16]:
foka = ['humidity High','humidity Low']
df['HUMI_cat'] = pd.qcut(df['HUMI'],2, labels=foka)
In [18]:
sns.set(style="ticks")
sns.relplot(x="TEMP", y="PM_Taiyuanjie",hue="Pres_cat", size="HUMI_cat", col='winter/summer',size_order=["humidity High", "humidity Low"],height=5, aspect=0.75,palette="rocket_r",legend="full",kind="line",data=df)
Out[18]: