[英]How to change frequency of x-axis tick label of datetime data in python
[英]Change frequency of x-axis tick label of datetime data in python bar chart using matplotlib
我有一個腳本,可以接收多個.csv文件並輸出多個條形圖。 數據為每日降雨量總計,因此x軸為日期格式為%d %m %Y
。 照原樣,該代碼嘗試將所有365天都包含在標簽中,但x軸被塞住了。 例如,我可以使用什么代碼來每月僅包含一個標簽,格式為“ Jan 01”。
import pandas as pd
import time
import os
import matplotlib.pyplot as plt
files = ['w.pod.csv',
't.pod.csv',
'r.pod.csv',
'n.pod.csv',
'm.pod.csv',
'k.pod.csv',
'j.pod.csv',
'h.pod.csv',
'g.pod.csv',
'c.pod.csv',
'b.pod.csv']
for f in files:
fn = f.split('.')[0]
dat = pd.read_csv(f)
df0 = dat.loc[:, ['TimeStamp', 'RF']]
# Change time format
df0["time"] = pd.to_datetime(df0["TimeStamp"])
df0["day"] = df0['time'].map(lambda x: x.day)
df0["month"] = df0['time'].map(lambda x: x.month)
df0["year"] = df0['time'].map(lambda x: x.year)
df0.to_csv('{}_1.csv'.format(fn), na_rep="0") # write to csv
# Combine for daily rainfall
df1 = pd.read_csv('{}_1.csv'.format(fn), encoding='latin-1',
usecols=['day', 'month', 'year', 'RF', 'TimeStamp'])
df2 = df1.groupby(['day', 'month', 'year'], as_index=False).sum()
df2.to_csv('{}_2.csv'.format(fn), na_rep="0", header=None) # write to csv
# parse date
df3 = pd.read_csv('{}_2.csv'.format(fn), header=None, index_col='datetime',
parse_dates={'datetime': [1,2,3]},
date_parser=lambda x: pd.datetime.strptime(x, '%d %m %Y'))
def dt_parse(date_string):
dt = pd.datetime.strptime(date_string, '%d %m %Y')
return dt
# sort datetime
df4 = df3.sort()
final = df4.reset_index()
# rename columns
final.columns = ['date', 'bleh', 'rf']
[![enter image description here][1]][1] final[['date','rf']].plot(kind='bar')
plt.suptitle('{} Rainfall 2015-2016'.format(fn), fontsize=20)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Rain / mm', fontsize=16)
plt.savefig('{}.png'.format(fn))
這是我先前問題的擴展: 使用多個.csv文件在python中自動制作多個圖
這並不容易,但這可行:
#sample df with dates of one year, rf are random integers
np.random.seed(100)
N = 365
start = pd.to_datetime('2015-02-24')
rng = pd.date_range(start, periods=N)
final = pd.DataFrame({'date': rng, 'rf': np.random.randint(50, size=N)})
print (final.head())
date rf
0 2015-02-24 8
1 2015-02-25 24
2 2015-02-26 3
3 2015-02-27 39
4 2015-02-28 23
fn = 'suptitle'
#rot - ratation of labels in axis x
ax = final.plot(x='date', y='rf', kind='bar', rot='45')
plt.suptitle('{} Rainfall 2015-2016'.format(fn), fontsize=20)
plt.xlabel('Date', fontsize=18)
plt.ylabel('Rain / mm', fontsize=16)
#set cusom format of dates
ticklabels = final.date.dt.strftime('%Y-%m-%d')
ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels))
#show only each 30th label, another are not visible
spacing = 30
visible = ax.xaxis.get_ticklabels()[::spacing]
for label in ax.xaxis.get_ticklabels():
if label not in visible:
label.set_visible(False)
plt.show()
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