text_plain_output_1.png
import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sn

daily_Data = pd.read_csv('../input/train.csv')

daily_Data.shape

daily_Data.dtypes

daily_Data.columns

daily_Data.isnull().sum()

f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="season", data=daily_Data, color="c");
plt.xlabel("season")

daily_Data.season.value_counts()

f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="holiday", data=daily_Data,color='c');
plt.xlabel("holiday")

daily_Data.holiday.value_counts()

f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="workingday",data=daily_Data,color='c');
plt.xlabel("workingday")

daily_Data.workingday.value_counts()

f, ax = plt.subplots(figsize=(5,5))
plt.hist(x="weather",data=daily_Data,color='c');
plt.xlabel("weather")

daily_Data.weather.value_counts()

corrMatt = daily_Data[['temp', 'atemp', 'casual', 'registered', 'humidity', 'windspeed', 'count']].corr()
mask = np.array(corrMatt)
mask[np.tril_indices_from(mask)] = False
fig, ax = plt.subplots()
fig.set_size_inches(20, 10)
sn.heatmap(corrMatt, mask=mask, vmax=0.8, square=True, annot=True)