Python Programming

import pandas as pd

df = pd.read_csv('sitka_weather_07-2021_simple.csv')
        

        df.index
        

    RangeIndex(start=0, stop=31, step=1)
    

    df.columns
    

    Index(['STATION', 'NAME', 'DATE', 'TAVG', 'TMAX', 'TMIN'], dtype='object')
    

    df.head()
    

    
        STATION                 NAME          DATE  TAVG TMAX TMIN
0   USW00025333 SITKA AIRPORT, AK US    2021-07-01   NaN   61   53
1   USW00025333 SITKA AIRPORT, AK US    2021-07-02   NaN   60   52
2   USW00025333 SITKA AIRPORT, AK US    2021-07-03   NaN   66   54
3   USW00025333 SITKA AIRPORT, AK US    2021-07-04   NaN   60   55
4   USW00025333 SITKA AIRPORT, AK US    2021-07-05   NaN   65   55
    

df["TMAX"]

0     61
1     60
...
29    73
30    66
Name: TMAX, dtype: int64

df.loc[0]

STATION             USW00025333
NAME       SITKA AIRPORT, AK US
DATE                 2021-07-01
TAVG                        NaN
TMAX                         61
TMIN                         53
Name: 0, dtype: object
                        

df.loc[:,'TAVG':'TMAX']   #查看连续多列
df.loc[:,['TAVG','TMAX']] #查看特定多列
df.loc[2:4,:]   # 查看连续多行 不是左闭右开
df.loc[[2,4],:] # 查看特定多行
                        

                        # calculation
                        df.loc[:, 'difference'] = df["TMAX"] - df["TMIN"] 

                        # example
                        df.loc[:, 'x'] = 1
                        df.loc[:, 'y'] = range(31)
                        

                        # 删除返回值
                        df_x = df.drop('difference', axis = 1) # 删除单列
                        df_y = df.drop(1, axis = 0)            # 删除单行
                        

                        df_60 = df[df["TMAX"] >= 60]
                        

from matplotlib import pyplot as plt

# Plot the high temperatures.
fig, ax = plt.subplots()
ax.plot(df["TMAX"], c='red')

# Format plot.
ax.set_title("Daily high temperatures, July 2021", fontsize=24)
ax.set_xlabel('', fontsize=16)
ax.set_ylabel("Temperature (F)", fontsize=16)
ax.tick_params(labelsize=16)

plt.savefig('simple.jpg',bbox_inches='tight',dpi=300)

# convert string to time format
df.loc[:,'date'] = pd.to_datetime(df['DATE'])
    

first_date = pd.to_datetime('2021-7-1',format = '%Y-%m-%d') 
    

%A Weekday name, such as Monday; %B Month name, such as January; 
%m Month, as a number (01 to 12)
%d Day of the month, as a number (01 to 31) 
%Y Four-digit year, such as 2015; %y Two-digit year, such as 15
%M Minutes (00 to 59); %S Seconds (00 to 61)
    

# filtering data by date
df_t = df[(df['date']>="20210715") & (df['date']<="20210720")]
df_t
    

14  USW00025333 SITKA AIRPORT, AK US    2021-07-15  NaN 60  50  2021-07-15
...
19  USW00025333 SITKA AIRPORT, AK US    2021-07-20  NaN 64  56  2021-07-20

    

# calculate periods
periods = df.loc[20, "date"] - df.loc[1, "date"]
periods.days
    

    19
    

df.loc[:,'date'] = pd.to_datetime(df['DATE'])

fig, ax = plt.subplots()
ax.plot(df["date"], df["TMAX"], c='red')

ax.set_title("Daily high temperatures, July 2021", fontsize=18)
ax.set_xlabel('', fontsize=16)
fig.autofmt_xdate()
ax.set_ylabel("Temperature (F)", fontsize=16)
ax.tick_params(labelsize=10)

plt.savefig('simple.jpg',bbox_inches='tight',dpi=300)

import pandas as pd
 
df = pd.read_csv('sitka_weather_2021_simple.csv')
df.loc[:,'date'] = pd.to_datetime(df['DATE'])

# Plot data.
fig, ax = plt.subplots()
ax.plot(df["date"], df["TMAX"], c='red', alpha=0.5)
ax.plot(df["date"], df["TMIN"], c='blue', alpha=0.5)
ax.fill_between(df["date"], df["TMAX"], df["TMIN"], facecolor='blue', alpha=0.1)

# Format plot.
ax.set_title("Daily high and low temperatures - 2021", fontsize=18)
ax.set_xlabel('', fontsize=16)
fig.autofmt_xdate()
ax.set_ylabel("Temperature (F)", fontsize=16)
ax.tick_params(labelsize=10)

plt.savefig('simple.jpg',bbox_inches='tight',dpi=300)

import pandas as pd
from matplotlib import pyplot as plt

frame = pd.read_csv('death_valley_2021_simple.csv')

frame.info()

  
RangeIndex: 365 entries, 0 to 364
Data columns (total 6 columns):
 #   Column   Non-Null Count  Dtype  
---  ------   --------------  -----  
 0   STATION  365 non-null    object 
 1   NAME     365 non-null    object 
 2   DATE     365 non-null    object 
 3   TMAX     364 non-null    float64
 4   TMIN     365 non-null    int64  
 5   TOBS     360 non-null    float64
dtypes: float64(2), int64(1), object(3)
memory usage: 17.2+ KB

                        frame[frame["TMAX"].isna()]
                        

123 USC00042319 DEATH VALLEY NATIONAL PARK, CA US   2021-05-04  NaN 72  89.0
                        

frame_DT = frame[["DATE", "TMAX", "TMIN"]]
frame_cleaned = frame_DT.dropna()
frame_cleaned.info()
                        

Index: 364 entries, 0 to 364
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   DATE    364 non-null    object 
 1   TMAX    364 non-null    float64
 2   TMIN    364 non-null    int64  
dtypes: float64(1), int64(1), object(1)
memory usage: 11.4+ KB
    

from pathlib import Path
import pandas as pd
import json

path = Path('btc_close_2017.json')
contents = path.read_text()
btc_data = json.loads(contents)

date = [];close=[]; months=[]
for btc_dict in btc_data:
    months.append(int(btc_dict['month']))
    close.append(float(btc_dict['close']))
    date.append(btc_dict['date'])

df = pd.DataFrame({"close": close, "months": months, "date": date})
df.loc[:,'date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.plot(df['date'],df['close'], linewidth=0.5, c='red')
ax.scatter(df['date'],df['close'], s=5, c='red')
ax.set_title('Close',fontsize=10)
fig.autofmt_xdate()

plt.savefig('close.jpg',dpi=300)

monthly_avg = df.groupby('months')['close'].mean()
# mean(), sum(), max(), min(), std()
def f(arr):
    return arr.max()-arr.min()
monthly_avg = df.groupby('months')['close'].apply(f)
                        

monthly_avg = df.groupby('months')['close'].mean()
monthly_avg
                        

months
1      6285.870968
2      7315.714286
...
11    51436.166667
12    91936.416667
Name: close, dtype: float64
                        

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

ax.plot(monthly_avg.index, monthly_avg.values, linewidth=1)
ax.scatter(monthly_avg.index, monthly_avg.values, s=20)
ax.set_title('Close',fontsize=10)

plt.savefig('monthly_avg.jpg',dpi=300)
                        

from pathlib import Path
import pandas as pd
import json
path = Path('btc_close_2017.json')
contents = path.read_text()
btc_data = json.loads(contents)
date = [];close=[]; months=[]
for btc_dict in btc_data:
    months.append(int(btc_dict['month']))
    close.append(float(btc_dict['close']))
    date.append(btc_dict['date'])
df = pd.DataFrame({"close": close, "months": months, "date": date})
df.loc[:,'date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')
                        

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot(df['date'],df['close'], linewidth=0.5, c='red')
fig.autofmt_xdate()

plt.savefig('close.jpg',dpi=300)